1341 Machine Learning Engineer jobs in Mumbai

Position: Lead MLOps Engineer

Experience: 9+ Years

Location: Mumbai, Mahape

Email ID for CV sharing:

About the Role

We are looking for a Lead MLOps Engineer who will lead and support the end-to-end deployment, monitoring, and optimization of machine learning and data-driven applications across cloud platforms. You will collaborate with data scientists, engineers, and business stakeholders to ensure scalable, secure, and highly available ML systems.

Key Responsibilities:

• Be a hands-on contributor capable of independently designing and developing complete MLOps solutions from scratch.
• Lead end-to-end ML pipeline development, deployment, and monitoring across GCP and Azure.
• Build and maintain CI/CD pipelines using tools like ArgoCD, Git, and Docker.
• Automate and optimize ML model training, validation, deployment, and scaling using Kubernetes, Kubeflow, or similar orchestration platforms.
• Develop data processing workflows using Python and PySpark on distributed systems.
• Implement observability using tools like Grafana, NewRelic, and cloud-native monitoring solutions.
• Collaborate with Data Scientists to transition research into production-grade solutions.
• Guide and mentor junior engineers, enforce coding standards, and conduct code reviews.
• Demonstrate business understanding to align ML pipelines with product goals.
• Manage infrastructure as code (IaC) for reproducibility and scalability.
• Exposure to AI and RAG-related development, various GPU and AI Platforms required.

Required Skills & Experience:

• 9+ years of hands-on experience in MLOps roles.
• Strong proficiency in Python and PySpark with clean and scalable code practices.
• Expertise in GCP and Azure cloud platforms – including compute, storage, and networking components.
• Proven experience in deploying and managing containerized applications using Docker and Kubernetes.
• Hands-on with CI/CD tools – preferably ArgoCD, GitHub Actions, or GitLab CI.
• Experience in monitoring, logging, and alerting using tools such as Grafana, NewRelic, Prometheus, or similar.
• Understanding of ML model lifecycle, versioning, and performance monitoring.
• Experience with MLFlow for model versioning.
• Ability to create REST APIs using FastAPI, Flask, or Django.
• Strong problem-solving, communication, and stakeholder management skills.
• Experience mentoring teams and driving end-to-end project execution.
• Exposure to Vertex AI Pipeline in GCP or similar in other clouds is a plus.

About the Role:We are looking for an experienced Machine Learning Engineer to design, build, and deploy ML models that power business-critical solutions. You will collaborate with cross-functional teams to deliver scalable ML systems, driving innovation and measurable business outcomes.Key Responsibilities:Design, develop, and deploy ML models (regression, classification, clustering, deep learning).Build and optimize data pipelines, feature engineering, and training workflows.Deploy models to production using Docker, Kubernetes, and cloud platforms (AWS/GCP/Azure).Monitor model performance, retrain, and maintain production-ready systems.Develop APIs and integrate ML solutions with enterprise applications.Stay updated on ML/AI trends and contribute innovative solutions.Collaborate with data scientists, engineers, and product managers to align with business goals.Qualifications:3+ years of experience in ML engineering or related field.Strong proficiency in Python; knowledge of Java/Scala/R is a plus.Expertise with ML libraries/frameworks: scikit-learn, Tensor Flow, Py Torch, Keras.Hands-on with data tools: Pandas, Num Py, Spark.Experience with ML deployment (Sage Maker, Azure ML, GCP AI, Tensor Flow Serving, Torch Serve).Familiarity with databases (SQL, Mongo DB, Cassandra) and version control (Git).Understanding of Dev Ops/MLOps practices.Excellent problem-solving, communication, and collaboration skills.

Join our team as a Machine Learning Engineer and build innovative solutions for our U. S. client. We're looking for someone with a strong background in machine learning who is ready to design, build, and deploy robust ML models in a production environment. Your expertise in AWS Sage Maker will be key to creating scalable solutions that deliver real business value.ResponsibilitiesDesign, develop, and deploy end-to-end machine learning pipelines on AWS Sage Maker.Collaborate with data scientists to translate models and algorithms into production-ready code.Manage the entire machine learning lifecycle, including data ingestion, model training, validation, and deployment.Implement MLOps best practices to automate workflows and ensure model performance and reliability.Optimize ML models for performance, scalability, and cost efficiency on the AWS platform.Monitor and troubleshoot deployed models to ensure they meet performance and business requirements.Stay current with the latest advancements in machine learning and cloud technologies.QualificationsBachelor's or Master's degree in Computer Science, Engineering, or a related field.3+ years of experience in a machine learning or data science role.Hands-on experience with AWS services, specifically Sage Maker.Proficiency in Python and standard ML libraries (e.g., scikit-learn, Tensor Flow, Py Torch).Experience with containerization technologies like Docker.Solid understanding of MLOps principles and tools for CI/CD.Knowledge of data engineering principles and experience with big data technologies (e.g., Spark, Hadoop).Strong problem-solving skills and the ability to work in a collaborative environment.Preferred QualificationsExperience with other AWS services such as S3, Lambda, Glue, and Redshift.Certifications such as AWS Certified Machine Learning – Specialty.Experience with model serving frameworks (e.g., Flask, Fast API).Experience with version control systems like Git.Familiarity with agile development methodologies.

We seek a Machine Learning Engineer colleague to join our world-class client, one of thebiggest and most advanced IT company.• Remote work• Employment/Contract Type: Contract• Location: India• Duration: 1 year (with potential to be extended)• Hours per Week: 40 (Full-time)Description:We are seeking an experienced Machine Learning Engineer to join our growing team ofconsultants. In this role, you will be responsible for designing, developing, and deploying machinelearning models and algorithms to solve complex business challenges with Databricks. You will workclosely with data scientists, data engineers, and business stakeholders to build scalable and efficientmachine learning solutions.Key Responsibilities● Design and implement machine learning models and algorithms with Databricks, includingpredictive analytics, natural language processing, computer vision, and more.● Lead the end-to-end development process, from data collection and preprocessing to modeldeployment and monitoring.● Collaborate with cross-functional teams to understand business requirements and translatethem into technical solutions.● Optimize and fine-tune models for performance, accuracy, and scalability in a productionenvironment.● Stay up-to-date with the latest advancements in machine learning and AI, and apply them toimprove existing systems.● Contribute to the development of machine learning infrastructure, including data pipelines,model serving frameworks, and monitoring tools.● Collaborate with client stakeholders to drive solutions to solve business problems.Qualifications● Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, or a relatedfield; Ph D is a plus.● 3+ years of experience in machine learning engineering or related fields.● 2+ years of experience with Databricks● Proficiency in programming languages such as Python, R, or Java, with a strong understandingof machine learning libraries and frameworks (e.g., Tensor Flow, Py Torch, scikit-learn).● Experience with cloud platforms and MLOps tools, such as MLflow, for model deployment andmonitoring.● Strong problem-solving skills and ability to work in a fast-paced environment.● Excellent communication and collaboration skills, with a track record of working effectively incross-functional teamsAbout DexianDexian is an Equal Opportunity Employer that recruits and hires qualified candidates without regard to race, religion, sex, sexual orientation, gender identity, age, national origin, ancestry, citizenship, disability, or veteran status.

Position: Lead MLOps Engineer

Experience: 9+ Years

Location: Mumbai, Mahape

Email ID for CV sharing:

About the Role

We are looking for a Lead MLOps Engineer who will lead and support the end-to-end deployment, monitoring, and optimization of machine learning and data-driven applications across cloud platforms. You will collaborate with data scientists, engineers, and business stakeholders to ensure scalable, secure, and highly available ML systems.

Key Responsibilities:

• Be a hands-on contributor capable of independently designing and developing complete MLOps solutions from scratch.
• Lead end-to-end ML pipeline development, deployment, and monitoring across GCP and Azure .
• Build and maintain CI/CD pipelines using tools like ArgoCD, Git, and Docker.
• Automate and optimize ML model training, validation, deployment, and scaling using Kubernetes, Kubeflow, or similar orchestration platforms .
• Develop data processing workflows using Python and PySpark on distributed systems.
• Implement observability using tools like Grafana, NewRelic , and cloud-native monitoring solutions.
• Collaborate with Data Scientists to transition research into production-grade solutions.
• Guide and mentor junior engineers, enforce coding standards, and conduct code reviews.
• Demonstrate business understanding to align ML pipelines with product goals.
• Manage infrastructure as code (IaC) for reproducibility and scalability.
• Exposure to AI and RAG-related development , various GPU and AI Platforms required.

Required Skills & Experience:

• 9+ years of hands-on experience in MLOps roles.
• Strong proficiency in Python and PySpark with clean and scalable code practices.
• Expertise in GCP and Azure cloud platforms – including compute, storage, and networking components.
• Proven experience in deploying and managing containerized applications using Docker and Kubernetes .
• Hands-on with CI/CD tools – preferably ArgoCD, GitHub Actions, or GitLab CI .
• Experience in monitoring, logging, and alerting using tools such as Grafana, NewRelic, Prometheus , or similar.
• Understanding of ML model lifecycle, versioning, and performance monitoring.
• Experience with MLFlow for model versioning.
• Ability to create REST APIs using FastAPI, Flask, or Django .
• Strong problem-solving, communication, and stakeholder management skills.
• Experience mentoring teams and driving end-to-end project execution.
• Exposure to Vertex AI Pipeline in GCP or similar in other clouds is a plus.