5,635 Frameworks jobs in India

The Artificial Intelligence (AI) Frameworks team at Microsoft develops the AI software used to train and deploy the world's most advanced AI models. We collaborate with our hardware teams and partners to build the software stacks for Microsoft's next-generation supercomputers and the new Maia-100 AI accelerator.  We work closely with ML researchers and developers to optimize and scale out model training and inference. 
The team operates at the intersection of AI algorithmic innovation, purpose-built AI hardware, systems, and software. We are a cross-disciplined team of highly capable and motivated people with a collaborative and inclusive culture.  We collaborate with hardware design team and build system software stack and developer tools (debugger, profiler, simulator) for the novel AI accelerator. 
Join our team to help develop system software and tools for large-scale AI model training and inference on new hardware.  
It requires hands-on software design and development skills. We're looking for someone who has a demonstrated history of solving hard technical problems and is motivated to tackle the hardest problems in building a full end-to-end AI stack.  An entrepreneurial approach and ability to take initiative and move fast are essential
**Responsibilities**
**Responsibilities:**
+ Identify / estimate work, schedule deliverables
+ Apply strong engineering principles for developing SW in modern languages.
+ Participate in design discussion and evaluation of alternatives
+ Collaborate broadly across multiple disciplines from hardware designers, tool developers, performance analysisand with MLapplication developers  
+ Owns the next gen features of Microsoft AI accelerator directly contributing to the business impact.
**Qualifications**
**Required Qualifications:**
+ B.S. or advanced degree in computer engineering, computer science, or related fields 
+ 2+ years of software development and experience of leading at least two commercialized features from requirement to deployment.
+ 2+ years' experience with C/C++  A
+ Exposure to NVIDIA/AMD development environment comprising CUDA / ROCm for Deep Neural Network training and inference development 
**Preferred Qualifications:**
+ A strong technical background and solid foundation in software engineering principles and architecture design  
+ Strong intellectual curiosity and passion about learning new technologies  
+ Great cross-team collaboration skills and the desire to collaborate in a team of researchers and developers  
+ Strong communication skills (both written and oral)
Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include but are not limited to the following specialized security screenings: Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.
#aiplatform
Microsoft is an equal opportunity employer. Consistent with applicable law, all qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations ( .

Our client is at the forefront of developing cutting-edge AI solutions and is seeking a highly accomplished Lead Machine Learning Engineer with specialized expertise in deep learning frameworks. This fully remote role offers the exciting opportunity to architect, build, and deploy advanced ML models that power innovative products and services. You will lead a team of talented engineers, driving research into production and tackling complex challenges in areas like computer vision, NLP, and reinforcement learning.

Responsibilities:

• Lead the design, development, and implementation of scalable machine learning systems and pipelines.
• Architect and optimize deep learning models using frameworks such as TensorFlow, PyTorch, or JAX.
• Develop and deploy production-ready ML solutions, ensuring reliability, performance, and scalability.
• Collaborate with research scientists to translate novel algorithms and experimental findings into robust engineering implementations.
• Mentor and guide a team of ML engineers, fostering best practices in coding, testing, and deployment.
• Define and implement MLOps strategies for model versioning, monitoring, and continuous integration/deployment.
• Conduct thorough code reviews and provide constructive feedback to team members.
• Optimize model performance for efficiency and effectiveness across various hardware platforms.
• Stay abreast of the latest advancements in machine learning, deep learning, and AI research.
• Contribute to the strategic technical direction of the ML engineering team and the broader organization.
• Troubleshoot and resolve complex technical issues in production ML systems.

Qualifications:

• Master's or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative field.
• Minimum of 7 years of experience in machine learning engineering, with a strong focus on deep learning.
• Proven experience in leading ML projects from research to production.
• Deep expertise in Python and proficiency with core ML libraries (e.g., scikit-learn, pandas, NumPy).
• Extensive hands-on experience with deep learning frameworks like TensorFlow, PyTorch, or Keras.
• Strong understanding of MLOps principles and tools (e.g., Docker, Kubernetes, CI/CD, MLflow).
• Experience with cloud platforms (AWS, Azure, GCP) and their ML services.
• Excellent problem-solving, analytical, and algorithmic thinking skills.
• Exceptional leadership, communication, and collaboration abilities, critical for a remote-first environment.
• Experience with large-scale data processing and distributed systems.

This is a unique opportunity to shape the future of AI in a leading-edge, remote-first company. Join a passionate team dedicated to pushing the boundaries of machine learning. The role will involve close collaboration with teams situated in and servicing areas around Bengaluru, Karnataka, IN .

**Avant de postuler à un emploi, sélectionnez votre langue de préférence parmi les options disponibles en haut à droite de cette page.**
Découvrez votre prochaine opportunité au sein d'une organisation qui compte parmi les 500 plus importantes entreprises mondiales. Envisagez des opportunités innovantes, découvrez notre culture enrichissante et travaillez avec des équipes talentueuses qui vous poussent à vous développer chaque jour. Nous savons ce qu'il faut faire pour diriger UPS vers l'avenir : des personnes passionnées dotées d'une combinaison unique de compétences. Si vous avez les qualités, de la motivation, de l'autonomie ou le leadership pour diriger des équipes, il existe des postes adaptés à vos aspirations et à vos compétences d'aujourd'hui et de demain.
**Fiche de poste :**
**About Machine Learning Engineering at UPS Technology:**
We're the obstacle overcomers, the problem get-arounders. From figuring it out to getting it done. our innovative culture demands "yes and how!" We are UPS. We are the United Problem Solvers.
Our Machine Learning Engineering teams use their expertise in data science, software engineering, and AI to build next-generation intelligent systems. These systems power our Smart Logistics Network, optimize UPS Airlines, and enhance Global Transportation Operations. We build scalable, production-grade ML solutions that move up to 38 million packages a day (4.7 billion annually), delivering measurable impact across the enterprise.
**About this Role:**
We are seeking passionate Senior Machine Learning Engineers to design, develop, and deploy ML models and pipelines that drive business outcomes. You'll work closely with data scientists, software engineers, and product teams to build intelligent systems that are robust, scalable, and aligned with UPS's strategic goals.
You will contribute across the full ML lifecycle-from data exploration and feature engineering to model training, evaluation, deployment, and monitoring. You'll also help shape our MLOps practices and mentor junior engineers.
**Key Responsibilities:**
+ Design, deploy, and maintain production-ready ML models and pipelines for real-world applications.
+ Build and scale ML pipelines using **Vertex AI Pipelines, Kubeflow, Airflow** , and manage infra-as-code with **Terraform/Helm** .
+ Implement **automated retraining, drift detection, and re-deployment** of ML models.
+ Develop CI/CD workflows (GitHub Actions, GitLab CI, Jenkins) tailored for ML.
+ Implement **model monitoring, observability, and alerting** across accuracy, latency, and cost.
+ Integrate and manage **feature stores, knowledge graphs, and vector databases** for advanced ML/RAG use cases.
+ Ensure pipelines are **secure, compliant, and cost-optimized** .
+ Drive adoption of MLOps best practices: develop and maintain workflows to ensure reproducibility, versioning, lineage tracking, governance.
+ Mentor junior engineers and contribute to long-term ML platform architecture design and technical roadmap.
+ Stay current with the latest ML research and apply new tools pragmatically to production systems.
+ Collaborate with product managers, DS, and engineers to **translate business problems into reliable ML systems** .
**Required Qualifications:**
Education
Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or related field (PhD is a plus).
Experience
+ 5+ years of experience in machine learning engineering, **MLOps, or large-scale AI/DS systems** .
+ Strong foundations in data structures, algorithms, and distributed systems.
+ **Proficient in Python** (scikit-learn, PyTorch, TensorFlow, XGBoost, etc.) and SQL.
+ Hands-on **experience building and deploying ML models at scale** in cloud environments (GCP Vertex AI, AWS SageMaker, Azure ML).
+ Experience with **containerization** (Docker, Kubernetes) and orchestration (Airflow, TFX, Kubeflow).
+ Familiarity **with CI/CD pipelines, infrastructure-as-code** (Terraform/Helm), and configuration management.
+ Experience **with big data and streaming technologies** (Spark, Flink, Kafka, Hive, Hadoop).
+ Practical exposure to **model observability** tools (Prometheus, Grafana, EvidentlyAI) and governance (WatsonX)
+ Strong understanding of **statistical methods, ML algorithms, and deep learning architectures** .
**Preferred**
+ Experience with real-time inference systems or low-latency streaming platforms (e.g. Kafka Streams).
+ Hands-on with feature stores and enterprise ML platforms (IBM WatsonX, Vertex AI).
+ Knowledge of model interpretability and fairness frameworks (SHAP, LIME, Fairlearn) and responsible AI principles.
+ Strong understanding of data/model governance, lineage tracking, and compliance frameworks.
+ Contributions to open-source ML/MLOps libraries or strong participation in ML competitions (e.g., Kaggle, NeurIPS).
+ Domain experience in Logistics, supply chain, or large-scale consumer platforms.
**Type de contrat:**
en CDI
_Chez UPS, égalité des chances, traitement équitable et environnement de travail inclusif sont des valeurs clefs auxquelles nous sommes attachés._

**Before you apply to a job, select your language preference from the options available at the top right of this page.**
Explore your next opportunity at a Fortune Global 500 organization. Envision innovative possibilities, experience our rewarding culture, and work with talented teams that help you become better every day. We know what it takes to lead UPS into tomorrow-people with a unique combination of skill + passion. If you have the qualities and drive to lead yourself or teams, there are roles ready to cultivate your skills and take you to the next level.
**Job Description:**
**About Machine Learning Engineering at UPS Technology:**
We're the obstacle overcomers, the problem get-arounders. From figuring it out to getting it done. our innovative culture demands "yes and how!" We are UPS. We are the United Problem Solvers.
Our Machine Learning Engineering teams use their expertise in data science, software engineering, and AI to build next-generation intelligent systems. These systems power our Smart Logistics Network, optimize UPS Airlines, and enhance Global Transportation Operations. We build scalable, production-grade ML solutions that move up to 38 million packages a day (4.7 billion annually), delivering measurable impact across the enterprise.
**About this Role:**
We are seeking passionate Senior Machine Learning Engineers to design, develop, and deploy ML models and pipelines that drive business outcomes. You'll work closely with data scientists, software engineers, and product teams to build intelligent systems that are robust, scalable, and aligned with UPS's strategic goals.
You will contribute across the full ML lifecycle-from data exploration and feature engineering to model training, evaluation, deployment, and monitoring. You'll also help shape our MLOps practices and mentor junior engineers.
**Key Responsibilities:**
+ Design, deploy, and maintain production-ready ML models and pipelines for real-world applications.
+ Build and scale ML pipelines using **Vertex AI Pipelines, Kubeflow, Airflow** , and manage infra-as-code with **Terraform/Helm** .
+ Implement **automated retraining, drift detection, and re-deployment** of ML models.
+ Develop CI/CD workflows (GitHub Actions, GitLab CI, Jenkins) tailored for ML.
+ Implement **model monitoring, observability, and alerting** across accuracy, latency, and cost.
+ Integrate and manage **feature stores, knowledge graphs, and vector databases** for advanced ML/RAG use cases.
+ Ensure pipelines are **secure, compliant, and cost-optimized** .
+ Drive adoption of MLOps best practices: develop and maintain workflows to ensure reproducibility, versioning, lineage tracking, governance.
+ Mentor junior engineers and contribute to long-term ML platform architecture design and technical roadmap.
+ Stay current with the latest ML research and apply new tools pragmatically to production systems.
+ Collaborate with product managers, DS, and engineers to **translate business problems into reliable ML systems** .
**Required Qualifications:**
Education
Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or related field (PhD is a plus).
Experience
+ 5+ years of experience in machine learning engineering, **MLOps, or large-scale AI/DS systems** .
+ Strong foundations in data structures, algorithms, and distributed systems.
+ **Proficient in Python** (scikit-learn, PyTorch, TensorFlow, XGBoost, etc.) and SQL.
+ Hands-on **experience building and deploying ML models at scale** in cloud environments (GCP Vertex AI, AWS SageMaker, Azure ML).
+ Experience with **containerization** (Docker, Kubernetes) and orchestration (Airflow, TFX, Kubeflow).
+ Familiarity **with CI/CD pipelines, infrastructure-as-code** (Terraform/Helm), and configuration management.
+ Experience **with big data and streaming technologies** (Spark, Flink, Kafka, Hive, Hadoop).
+ Practical exposure to **model observability** tools (Prometheus, Grafana, EvidentlyAI) and governance (WatsonX)
+ Strong understanding of **statistical methods, ML algorithms, and deep learning architectures** .
**Preferred**
+ Experience with real-time inference systems or low-latency streaming platforms (e.g. Kafka Streams).
+ Hands-on with feature stores and enterprise ML platforms (IBM WatsonX, Vertex AI).
+ Knowledge of model interpretability and fairness frameworks (SHAP, LIME, Fairlearn) and responsible AI principles.
+ Strong understanding of data/model governance, lineage tracking, and compliance frameworks.
+ Contributions to open-source ML/MLOps libraries or strong participation in ML competitions (e.g., Kaggle, NeurIPS).
+ Domain experience in Logistics, supply chain, or large-scale consumer platforms.
**Employee Type:**
Permanent
UPS is committed to providing a workplace free of discrimination, harassment, and retaliation.

**Before you apply to a job, select your language preference from the options available at the top right of this page.**
Explore your next opportunity at a Fortune Global 500 organization. Envision innovative possibilities, experience our rewarding culture, and work with talented teams that help you become better every day. We know what it takes to lead UPS into tomorrow-people with a unique combination of skill + passion. If you have the qualities and drive to lead yourself or teams, there are roles ready to cultivate your skills and take you to the next level.
**Job Description:**
**About Machine Learning Engineering at UPS Technology:**
We're the obstacle overcomers, the problem get-arounders. From figuring it out to getting it done. our innovative culture demands "yes and how!" We are UPS. We are the United Problem Solvers.
Our Machine Learning Engineering teams use their expertise in data science, software engineering, and AI to build next-generation intelligent systems. These systems power our Smart Logistics Network, optimize UPS Airlines, and enhance Global Transportation Operations. We build scalable, production-grade ML solutions that move up to 38 million packages a day (4.7 billion annually), delivering measurable impact across the enterprise.
**About this Role:**
We are seeking passionate Senior Machine Learning Engineers to design, develop, and deploy ML models and pipelines that drive business outcomes. You'll work closely with data scientists, software engineers, and product teams to build intelligent systems that are robust, scalable, and aligned with UPS's strategic goals.
You will contribute across the full ML lifecycle-from data exploration and feature engineering to model training, evaluation, deployment, and monitoring. You'll also help shape our MLOps practices and mentor junior engineers.
**Key Responsibilities:**
+ Design, deploy, and maintain production-ready ML models and pipelines for real-world applications.
+ Build and scale ML pipelines using **Vertex AI Pipelines, Kubeflow, Airflow** , and manage infra-as-code with **Terraform/Helm** .
+ Implement **automated retraining, drift detection, and re-deployment** of ML models.
+ Develop CI/CD workflows (GitHub Actions, GitLab CI, Jenkins) tailored for ML.
+ Implement **model monitoring, observability, and alerting** across accuracy, latency, and cost.
+ Integrate and manage **feature stores, knowledge graphs, and vector databases** for advanced ML/RAG use cases.
+ Ensure pipelines are **secure, compliant, and cost-optimized** .
+ Drive adoption of MLOps best practices: develop and maintain workflows to ensure reproducibility, versioning, lineage tracking, governance.
+ Mentor junior engineers and contribute to long-term ML platform architecture design and technical roadmap.
+ Stay current with the latest ML research and apply new tools pragmatically to production systems.
+ Collaborate with product managers, DS, and engineers to **translate business problems into reliable ML systems** .
**Required Qualifications:**
Education
Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or related field (PhD is a plus).
Experience
+ 5+ years of experience in machine learning engineering, **MLOps, or large-scale AI/DS systems** .
+ Strong foundations in data structures, algorithms, and distributed systems.
+ **Proficient in Python** (scikit-learn, PyTorch, TensorFlow, XGBoost, etc.) and SQL.
+ Hands-on **experience building and deploying ML models at scale** in cloud environments (GCP Vertex AI, AWS SageMaker, Azure ML).
+ Experience with **containerization** (Docker, Kubernetes) and orchestration (Airflow, TFX, Kubeflow).
+ Familiarity **with CI/CD pipelines, infrastructure-as-code** (Terraform/Helm), and configuration management.
+ Experience **with big data and streaming technologies** (Spark, Flink, Kafka, Hive, Hadoop).
+ Practical exposure to **model observability** tools (Prometheus, Grafana, EvidentlyAI) and governance (WatsonX)
+ Strong understanding of **statistical methods, ML algorithms, and deep learning architectures** .
**Preferred**
+ Experience with real-time inference systems or low-latency streaming platforms (e.g. Kafka Streams).
+ Hands-on with feature stores and enterprise ML platforms (IBM WatsonX, Vertex AI).
+ Knowledge of model interpretability and fairness frameworks (SHAP, LIME, Fairlearn) and responsible AI principles.
+ Strong understanding of data/model governance, lineage tracking, and compliance frameworks.
+ Contributions to open-source ML/MLOps libraries or strong participation in ML competitions (e.g., Kaggle, NeurIPS).
+ Domain experience in Logistics, supply chain, or large-scale consumer platforms.
**Employee Type:**
Permanent
UPS is committed to providing a workplace free of discrimination, harassment, and retaliation.

**Avant de postuler à un emploi, sélectionnez votre langue de préférence parmi les options disponibles en haut à droite de cette page.**
Découvrez votre prochaine opportunité au sein d'une organisation qui compte parmi les 500 plus importantes entreprises mondiales. Envisagez des opportunités innovantes, découvrez notre culture enrichissante et travaillez avec des équipes talentueuses qui vous poussent à vous développer chaque jour. Nous savons ce qu'il faut faire pour diriger UPS vers l'avenir : des personnes passionnées dotées d'une combinaison unique de compétences. Si vous avez les qualités, de la motivation, de l'autonomie ou le leadership pour diriger des équipes, il existe des postes adaptés à vos aspirations et à vos compétences d'aujourd'hui et de demain.
**Fiche de poste :**
**About Machine Learning Engineering at UPS Technology:**
We're the obstacle overcomers, the problem get-arounders. From figuring it out to getting it done. our innovative culture demands "yes and how!" We are UPS. We are the United Problem Solvers.
Our Machine Learning Engineering teams use their expertise in data science, software engineering, and AI to build next-generation intelligent systems. These systems power our Smart Logistics Network, optimize UPS Airlines, and enhance Global Transportation Operations. We build scalable, production-grade ML solutions that move up to 38 million packages a day (4.7 billion annually), delivering measurable impact across the enterprise.
**About this Role:**
We are seeking passionate Senior Machine Learning Engineers to design, develop, and deploy ML models and pipelines that drive business outcomes. You'll work closely with data scientists, software engineers, and product teams to build intelligent systems that are robust, scalable, and aligned with UPS's strategic goals.
You will contribute across the full ML lifecycle-from data exploration and feature engineering to model training, evaluation, deployment, and monitoring. You'll also help shape our MLOps practices and mentor junior engineers.
**Key Responsibilities:**
+ Design, deploy, and maintain production-ready ML models and pipelines for real-world applications.
+ Build and scale ML pipelines using **Vertex AI Pipelines, Kubeflow, Airflow** , and manage infra-as-code with **Terraform/Helm** .
+ Implement **automated retraining, drift detection, and re-deployment** of ML models.
+ Develop CI/CD workflows (GitHub Actions, GitLab CI, Jenkins) tailored for ML.
+ Implement **model monitoring, observability, and alerting** across accuracy, latency, and cost.
+ Integrate and manage **feature stores, knowledge graphs, and vector databases** for advanced ML/RAG use cases.
+ Ensure pipelines are **secure, compliant, and cost-optimized** .
+ Drive adoption of MLOps best practices: develop and maintain workflows to ensure reproducibility, versioning, lineage tracking, governance.
+ Mentor junior engineers and contribute to long-term ML platform architecture design and technical roadmap.
+ Stay current with the latest ML research and apply new tools pragmatically to production systems.
+ Collaborate with product managers, DS, and engineers to **translate business problems into reliable ML systems** .
**Required Qualifications:**
Education
Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or related field (PhD is a plus).
Experience
+ 5+ years of experience in machine learning engineering, **MLOps, or large-scale AI/DS systems** .
+ Strong foundations in data structures, algorithms, and distributed systems.
+ **Proficient in Python** (scikit-learn, PyTorch, TensorFlow, XGBoost, etc.) and SQL.
+ Hands-on **experience building and deploying ML models at scale** in cloud environments (GCP Vertex AI, AWS SageMaker, Azure ML).
+ Experience with **containerization** (Docker, Kubernetes) and orchestration (Airflow, TFX, Kubeflow).
+ Familiarity **with CI/CD pipelines, infrastructure-as-code** (Terraform/Helm), and configuration management.
+ Experience **with big data and streaming technologies** (Spark, Flink, Kafka, Hive, Hadoop).
+ Practical exposure to **model observability** tools (Prometheus, Grafana, EvidentlyAI) and governance (WatsonX)
+ Strong understanding of **statistical methods, ML algorithms, and deep learning architectures** .
**Preferred**
+ Experience with real-time inference systems or low-latency streaming platforms (e.g. Kafka Streams).
+ Hands-on with feature stores and enterprise ML platforms (IBM WatsonX, Vertex AI).
+ Knowledge of model interpretability and fairness frameworks (SHAP, LIME, Fairlearn) and responsible AI principles.
+ Strong understanding of data/model governance, lineage tracking, and compliance frameworks.
+ Contributions to open-source ML/MLOps libraries or strong participation in ML competitions (e.g., Kaggle, NeurIPS).
+ Domain experience in Logistics, supply chain, or large-scale consumer platforms.
**Type de contrat:**
en CDI
_Chez UPS, égalité des chances, traitement équitable et environnement de travail inclusif sont des valeurs clefs auxquelles nous sommes attachés._

**Before you apply to a job, select your language preference from the options available at the top right of this page.**
Explore your next opportunity at a Fortune Global 500 organization. Envision innovative possibilities, experience our rewarding culture, and work with talented teams that help you become better every day. We know what it takes to lead UPS into tomorrow-people with a unique combination of skill + passion. If you have the qualities and drive to lead yourself or teams, there are roles ready to cultivate your skills and take you to the next level.
**Job Description:**
**About Machine Learning Engineering at UPS Technology:**
We're the obstacle overcomers, the problem get-arounders. From figuring it out to getting it done. our innovative culture demands "yes and how!" We are UPS. We are the United Problem Solvers.
Our Machine Learning Engineering teams use their expertise in data science, software engineering, and AI to build next-generation intelligent systems. These systems power our Smart Logistics Network, optimize UPS Airlines, and enhance Global Transportation Operations. We build scalable, production-grade ML solutions that move up to 38 million packages a day (4.7 billion annually), delivering measurable impact across the enterprise.
**About this Role:**
We are seeking passionate Senior Machine Learning Engineers to design, develop, and deploy ML models and pipelines that drive business outcomes. You'll work closely with data scientists, software engineers, and product teams to build intelligent systems that are robust, scalable, and aligned with UPS's strategic goals.
You will contribute across the full ML lifecycle-from data exploration and feature engineering to model training, evaluation, deployment, and monitoring. You'll also help shape our MLOps practices and mentor junior engineers.
**Key Responsibilities:**
+ Design, deploy, and maintain production-ready ML models and pipelines for real-world applications.
+ Build and scale ML pipelines using **Vertex AI Pipelines, Kubeflow, Airflow** , and manage infra-as-code with **Terraform/Helm** .
+ Implement **automated retraining, drift detection, and re-deployment** of ML models.
+ Develop CI/CD workflows (GitHub Actions, GitLab CI, Jenkins) tailored for ML.
+ Implement **model monitoring, observability, and alerting** across accuracy, latency, and cost.
+ Integrate and manage **feature stores, knowledge graphs, and vector databases** for advanced ML/RAG use cases.
+ Ensure pipelines are **secure, compliant, and cost-optimized** .
+ Drive adoption of MLOps best practices: develop and maintain workflows to ensure reproducibility, versioning, lineage tracking, governance.
+ Mentor junior engineers and contribute to long-term ML platform architecture design and technical roadmap.
+ Stay current with the latest ML research and apply new tools pragmatically to production systems.
+ Collaborate with product managers, DS, and engineers to **translate business problems into reliable ML systems** .
**Required Qualifications:**
Education
Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or related field (PhD is a plus).
Experience
+ 3+ years of experience in machine learning engineering, **MLOps, or large-scale AI/DS systems** .
+ Strong foundations in data structures, algorithms, and distributed systems.
+ **Proficient in Python** (scikit-learn, PyTorch, TensorFlow, XGBoost, etc.) and SQL.
+ Hands-on **experience building and deploying ML models at scale** in cloud environments (GCP Vertex AI, AWS SageMaker, Azure ML).
+ Experience with **containerization** (Docker, Kubernetes) and orchestration (Airflow, TFX, Kubeflow).
+ Familiarity **with CI/CD pipelines, infrastructure-as-code** (Terraform/Helm), and configuration management.
+ Experience **with big data and streaming technologies** (Spark, Flink, Kafka, Hive, Hadoop).
+ Practical exposure to **model observability** tools (Prometheus, Grafana, EvidentlyAI) and governance (WatsonX)
+ Strong understanding of **statistical methods, ML algorithms, and deep learning architectures** .
**Preferred**
+ Experience with real-time inference systems or low-latency streaming platforms (e.g. Kafka Streams).
+ Hands-on with feature stores and enterprise ML platforms (IBM WatsonX, Vertex AI).
+ Knowledge of model interpretability and fairness frameworks (SHAP, LIME, Fairlearn) and responsible AI principles.
+ Strong understanding of data/model governance, lineage tracking, and compliance frameworks.
+ Contributions to open-source ML/MLOps libraries or strong participation in ML competitions (e.g., Kaggle, NeurIPS).
+ Domain experience in Logistics, supply chain, or large-scale consumer platforms.
**Employee Type:**
Permanent
UPS is committed to providing a workplace free of discrimination, harassment, and retaliation.

**Avant de postuler à un emploi, sélectionnez votre langue de préférence parmi les options disponibles en haut à droite de cette page.**
Découvrez votre prochaine opportunité au sein d'une organisation qui compte parmi les 500 plus importantes entreprises mondiales. Envisagez des opportunités innovantes, découvrez notre culture enrichissante et travaillez avec des équipes talentueuses qui vous poussent à vous développer chaque jour. Nous savons ce qu'il faut faire pour diriger UPS vers l'avenir : des personnes passionnées dotées d'une combinaison unique de compétences. Si vous avez les qualités, de la motivation, de l'autonomie ou le leadership pour diriger des équipes, il existe des postes adaptés à vos aspirations et à vos compétences d'aujourd'hui et de demain.
**Fiche de poste :**
**About Machine Learning Engineering at UPS Technology:**
We're the obstacle overcomers, the problem get-arounders. From figuring it out to getting it done. our innovative culture demands "yes and how!" We are UPS. We are the United Problem Solvers.
Our Machine Learning Engineering teams use their expertise in data science, software engineering, and AI to build next-generation intelligent systems. These systems power our Smart Logistics Network, optimize UPS Airlines, and enhance Global Transportation Operations. We build scalable, production-grade ML solutions that move up to 38 million packages a day (4.7 billion annually), delivering measurable impact across the enterprise.
**About this Role:**
We are seeking passionate Senior Machine Learning Engineers to design, develop, and deploy ML models and pipelines that drive business outcomes. You'll work closely with data scientists, software engineers, and product teams to build intelligent systems that are robust, scalable, and aligned with UPS's strategic goals.
You will contribute across the full ML lifecycle-from data exploration and feature engineering to model training, evaluation, deployment, and monitoring. You'll also help shape our MLOps practices and mentor junior engineers.
**Key Responsibilities:**
+ Design, deploy, and maintain production-ready ML models and pipelines for real-world applications.
+ Build and scale ML pipelines using **Vertex AI Pipelines, Kubeflow, Airflow** , and manage infra-as-code with **Terraform/Helm** .
+ Implement **automated retraining, drift detection, and re-deployment** of ML models.
+ Develop CI/CD workflows (GitHub Actions, GitLab CI, Jenkins) tailored for ML.
+ Implement **model monitoring, observability, and alerting** across accuracy, latency, and cost.
+ Integrate and manage **feature stores, knowledge graphs, and vector databases** for advanced ML/RAG use cases.
+ Ensure pipelines are **secure, compliant, and cost-optimized** .
+ Drive adoption of MLOps best practices: develop and maintain workflows to ensure reproducibility, versioning, lineage tracking, governance.
+ Mentor junior engineers and contribute to long-term ML platform architecture design and technical roadmap.
+ Stay current with the latest ML research and apply new tools pragmatically to production systems.
+ Collaborate with product managers, DS, and engineers to **translate business problems into reliable ML systems** .
**Required Qualifications:**
Education
Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or related field (PhD is a plus).
Experience
+ 3+ years of experience in machine learning engineering, **MLOps, or large-scale AI/DS systems** .
+ Strong foundations in data structures, algorithms, and distributed systems.
+ **Proficient in Python** (scikit-learn, PyTorch, TensorFlow, XGBoost, etc.) and SQL.
+ Hands-on **experience building and deploying ML models at scale** in cloud environments (GCP Vertex AI, AWS SageMaker, Azure ML).
+ Experience with **containerization** (Docker, Kubernetes) and orchestration (Airflow, TFX, Kubeflow).
+ Familiarity **with CI/CD pipelines, infrastructure-as-code** (Terraform/Helm), and configuration management.
+ Experience **with big data and streaming technologies** (Spark, Flink, Kafka, Hive, Hadoop).
+ Practical exposure to **model observability** tools (Prometheus, Grafana, EvidentlyAI) and governance (WatsonX)
+ Strong understanding of **statistical methods, ML algorithms, and deep learning architectures** .
**Preferred**
+ Experience with real-time inference systems or low-latency streaming platforms (e.g. Kafka Streams).
+ Hands-on with feature stores and enterprise ML platforms (IBM WatsonX, Vertex AI).
+ Knowledge of model interpretability and fairness frameworks (SHAP, LIME, Fairlearn) and responsible AI principles.
+ Strong understanding of data/model governance, lineage tracking, and compliance frameworks.
+ Contributions to open-source ML/MLOps libraries or strong participation in ML competitions (e.g., Kaggle, NeurIPS).
+ Domain experience in Logistics, supply chain, or large-scale consumer platforms.
**Type de contrat:**
en CDI
_Chez UPS, égalité des chances, traitement équitable et environnement de travail inclusif sont des valeurs clefs auxquelles nous sommes attachés._

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Découvrez votre prochaine opportunité au sein d'une organisation qui compte parmi les 500 plus importantes entreprises mondiales. Envisagez des opportunités innovantes, découvrez notre culture enrichissante et travaillez avec des équipes talentueuses qui vous poussent à vous développer chaque jour. Nous savons ce qu'il faut faire pour diriger UPS vers l'avenir : des personnes passionnées dotées d'une combinaison unique de compétences. Si vous avez les qualités, de la motivation, de l'autonomie ou le leadership pour diriger des équipes, il existe des postes adaptés à vos aspirations et à vos compétences d'aujourd'hui et de demain.
**Fiche de poste :**
**About Machine Learning Engineering at UPS Technology:**
We're the obstacle overcomers, the problem get-arounders. From figuring it out to getting it done. our innovative culture demands "yes and how!" We are UPS. We are the United Problem Solvers.
Our Machine Learning Engineering teams use their expertise in data science, software engineering, and AI to build next-generation intelligent systems. These systems power our Smart Logistics Network, optimize UPS Airlines, and enhance Global Transportation Operations. We build scalable, production-grade ML solutions that move up to 38 million packages a day (4.7 billion annually), delivering measurable impact across the enterprise.
**About this Role:**
We are seeking passionate Senior Machine Learning Engineers to design, develop, and deploy ML models and pipelines that drive business outcomes. You'll work closely with data scientists, software engineers, and product teams to build intelligent systems that are robust, scalable, and aligned with UPS's strategic goals.
You will contribute across the full ML lifecycle-from data exploration and feature engineering to model training, evaluation, deployment, and monitoring. You'll also help shape our MLOps practices and mentor junior engineers.
**Key Responsibilities:**
+ Design, deploy, and maintain production-ready ML models and pipelines for real-world applications.
+ Build and scale ML pipelines using **Vertex AI Pipelines, Kubeflow, Airflow** , and manage infra-as-code with **Terraform/Helm** .
+ Implement **automated retraining, drift detection, and re-deployment** of ML models.
+ Develop CI/CD workflows (GitHub Actions, GitLab CI, Jenkins) tailored for ML.
+ Implement **model monitoring, observability, and alerting** across accuracy, latency, and cost.
+ Integrate and manage **feature stores, knowledge graphs, and vector databases** for advanced ML/RAG use cases.
+ Ensure pipelines are **secure, compliant, and cost-optimized** .
+ Drive adoption of MLOps best practices: develop and maintain workflows to ensure reproducibility, versioning, lineage tracking, governance.
+ Mentor junior engineers and contribute to long-term ML platform architecture design and technical roadmap.
+ Stay current with the latest ML research and apply new tools pragmatically to production systems.
+ Collaborate with product managers, DS, and engineers to **translate business problems into reliable ML systems** .
**Required Qualifications:**
Education
Bachelor's or Master's degree in Computer Science, Engineering, Mathematics, or related field (PhD is a plus).
Experience
+ 5+ years of experience in machine learning engineering, **MLOps, or large-scale AI/DS systems** .
+ Strong foundations in data structures, algorithms, and distributed systems.
+ **Proficient in Python** (scikit-learn, PyTorch, TensorFlow, XGBoost, etc.) and SQL.
+ Hands-on **experience building and deploying ML models at scale** in cloud environments (GCP Vertex AI, AWS SageMaker, Azure ML).
+ Experience with **containerization** (Docker, Kubernetes) and orchestration (Airflow, TFX, Kubeflow).
+ Familiarity **with CI/CD pipelines, infrastructure-as-code** (Terraform/Helm), and configuration management.
+ Experience **with big data and streaming technologies** (Spark, Flink, Kafka, Hive, Hadoop).
+ Practical exposure to **model observability** tools (Prometheus, Grafana, EvidentlyAI) and governance (WatsonX)
+ Strong understanding of **statistical methods, ML algorithms, and deep learning architectures** .
**Preferred**
+ Experience with real-time inference systems or low-latency streaming platforms (e.g. Kafka Streams).
+ Hands-on with feature stores and enterprise ML platforms (IBM WatsonX, Vertex AI).
+ Knowledge of model interpretability and fairness frameworks (SHAP, LIME, Fairlearn) and responsible AI principles.
+ Strong understanding of data/model governance, lineage tracking, and compliance frameworks.
+ Contributions to open-source ML/MLOps libraries or strong participation in ML competitions (e.g., Kaggle, NeurIPS).
+ Domain experience in Logistics, supply chain, or large-scale consumer platforms.
**Type de contrat:**
en CDI
_Chez UPS, égalité des chances, traitement équitable et environnement de travail inclusif sont des valeurs clefs auxquelles nous sommes attachés._