338 Machine Learning Engineer jobs in Coimbatore

Job Description: ML Engineer / Gen AI Engineer

Client : UBS

Location : Pune, India

Position Overview

We are seeking a highly skilled ML Engineer / Gen AI Engineer with a strong foundation in Machine Learning, Graph algorithms, SQL, and Generative AI. The ideal candidate will design, develop, and deploy robust, scalable solutions, collaborating with cross-functional teams to drive innovation in AI-driven applications for UBS.

Key Responsibilities

• Develop and Optimize ML Models : Design, implement, and fine-tune machine learning models using Python libraries such as TensorFlow, PyTorch, or scikit-learn to solve real-world financial problems.
• Graph-Based Solutions : Build and optimize graph-based algorithms and data structures using libraries like NetworkX or PyG (PyTorch Geometric) for applications such as network analysis, fraud detection, or knowledge graphs.
• SQL and Data Management : Write complex SQL queries to manage, transform, and analyze large datasets, ensuring efficient data pipelines and integration with databases like PostgreSQL, MySQL, or SQLite.
• Generative AI Development : Design and implement generative AI models (e.g., LLMs, GANs, or VAEs) using frameworks like Hugging Face, LangChain, or custom solutions for tasks such as text generation, data augmentation, or synthetic data creation.
• Code Quality and Scalability : Write clean, modular, and maintainable Python code, adhering to best practices, and optimize for performance and scalability in production environments.
• Collaboration and Innovation : Work closely with data scientists, engineers, and business teams at UBS to translate requirements into technical solutions, contributing to architectural decisions and innovative AI strategies.
• Data Pipeline Development : Build and maintain ETL pipelines to preprocess and integrate data for machine learning and graph-based applications, using tools like Apache Airflow or Pandas.
• Model Deployment : Deploy machine learning and generative AI models to production environments using tools like Docker, Kubernetes, or cloud platforms (AWS, GCP, Azure).
• Research and Stay Current : Stay updated on advancements in machine learning, graph theory, and generative AI, applying cutting-edge techniques to enhance project outcomes.

Required Qualifications

• Education : Bachelor’s or Master’s degree in Computer Science, Data Science, Mathematics, or a related field. PhD is a plus.
• Experience :
• 5+ years of professional Python programming experience.
• 3+ years of hands-on experience in machine learning model development and deployment.
• Proven expertise in graph algorithms, graph databases (e.g., Neo4j), or graph-based machine learning.
• Strong proficiency in SQL and relational database management.
• 2+ years working with generative AI models (e.g., LLMs, GANs, or diffusion models).

Technical Skills :

• Expert-level Python programming (e.g., Pandas, NumPy, scikit-learn, TensorFlow, PyTorch).
• Experience with graph libraries (e.g., NetworkX, PyG, or DGL) and graph databases.
• Advanced SQL skills for querying and optimizing large datasets.
• Familiarity with generative AI frameworks (e.g., Hugging Face, LangChain, or OpenAI APIs).
• Proficiency in version control (Git), CI/CD pipelines, and containerization (Docker).
• Experience with cloud platforms (AWS, GCP, or Azure) for model deployment.

Soft Skills :

• Strong problem-solving and analytical skills.
• Excellent communication and collaboration abilities.
• Ability to work in a fast-paced, innovative environment.

Preferred Qualifications

• Experience with large-scale distributed systems and big data frameworks (e.g., Spark, Hadoop).
• Familiarity with MLOps tools (e.g., MLflow, Kubeflow) for model lifecycle management.
• Knowledge of advanced graph algorithms (e.g., community detection, shortest path, centrality measures).
• Contributions to open-source AI or graph-related projects.
• Experience with real-time or streaming data processing.
• Familiarity with financial services or banking domain challenges.

Why Join Us?

• Work on cutting-edge AI and graph-based projects with real-world impact in the financial sector.
• Collaborate with a talented, global team in a supportive and innovative environment at UBS.
• Competitive salary, comprehensive benefits, and opportunities for professional growth.
• Access to state-of-the-art tools and technologies to fuel your expertise.

What You’ll Do

• Collaborate with research teams to prepare datasets for training and evaluation.
• Implement and run model training workflows, including hyperparameter tuning.
• Perform rigorous evaluation of models against benchmark datasets.
• Build and maintain pipelines for data preprocessing, feature extraction, and dataset versioning.
• Contribute to model deployment and monitoring in production environments.

Who You Are

• 2–5 years experience in machine learning engineering or applied data science.
• Strong programming skills in Python (PyTorch, TensorFlow, or equivalent).
• Experience with data wrangling and large dataset preparation.
• Ability to collaborate closely with ML researchers on experimental workflows.
• Comfortable with cloud environments (AWS/GCP/Azure) and containerization (Docker).

Machine Learning Engineer

About Astreya:

Astreya offers comprehensive IT support and managed services. These services include Data

Center and Network Management, Digital Workplace Services (like Service Desk, Audio Visual, and

IT Asset Management), as well as Next-Gen Digital Engineering services encompassing Software

Engineering, Data Engineering, and cybersecurity solutions. Astreya's expertise lies in creating

seamless interactions between people and technology to help organizations achieve operational

excellence and growth.

Responsibilities:

• Design, develop, and deploy machine learning models and algorithms to solve complex business problems and drive data-driven decision making
• Collaborate with cross-functional teams including data engineers, software engineers, and business stakeholders to understand requirements and translate business objectives into scalable ML solutions
• Build and maintain robust ML pipelines for data preprocessing, feature engineering, model training, validation, and deployment
• Optimize model performance through hyperparameter tuning, feature selection, and algorithmic improvements
• Implement MLOps best practices including model versioning, monitoring, and automated retraining workflows
• Ensure data quality, integrity, and security throughout the ML lifecycle
• Scale ML systems to handle large datasets and high-throughput inference requirements
• Conduct A/B testing and experimentation to validate model performance and business impact
• Monitor deployed models for drift, performance degradation, and bias detection
• Create comprehensive documentation including technical specifications, model cards, and deployment guides for stakeholders

Professional & Technical Skills:

• Must have strong proficiency in Python and experience with core ML libraries such as scikit-learn, XGBoost, LightGBM, and CatBoost
• Must have hands-on experience with deep learning frameworks including TensorFlow, PyTorch
• Must have solid understanding of machine learning algorithms, statistical methods, and model evaluation techniques
• Must have experience with cloud platforms (AWS, GCP, or Azure) and containerization technologies Docker or Kubernetes
• Need to have proficiency in SQL and experience working with both structured and unstructured datasets
• Need to have knowledge of big data technologies such as Spark, Hadoop, or distributed computing frameworks
• Must be familiar with MLOps tools and platforms such as MLflow, Kubeflow, or similar model lifecycle management systems
• Must have experience with version control systems (Git) and CI/CD pipelines for ML workflows
• Need to have knowledge of data visualization tools and libraries such as Matplotlib, Seaborn, Plotly, or Tableau
• Must be familiar with software engineering best practices, including code testing, debugging, and performance optimization
• Need to have understanding of software development methodologies such as Agile or Scrum
• Possess excellent analytical and problem-solving skills with ability to work independently and in team environments

Additional Information:

• Must have Bachelor's or Master's degree in Computer Science, Machine Learning, Statistics, Mathematics, or related quantitative field
• Preferred 3+ years of experience in machine learning engineering or related roles
• Preferred experience with real-time inference systems and model serving architectures

Preferred knowledge of specialized domains such as computer vision, natural language processing, or recommendation systems

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, TensorFlow, PyTorch, Keras .
• Hands-on with data tools: Pandas, NumPy, Spark .
• Experience with ML deployment (SageMaker, Azure ML, GCP AI, TensorFlow Serving, TorchServe).
• Familiarity with databases (SQL, MongoDB, Cassandra ) and version control (Git ).
• Understanding of DevOps/MLOps practices.
• Excellent problem-solving, communication, and collaboration skills.

Position- Machine Learning Engineer

Experience - 3 to 5 years

Job Location - Pune / Remote

Immediate Joiner Only

Role Overview

The ML Ops Engineer plays a critical role in the development, automation, and deployment of Machine Learning (ML) and Generative AI (GenAI) pipelines across AWS cloud environments. This hands-on role emphasizes building reproducible workflows, integrating observability tools, and enabling efficient, scalable model delivery. The position supports AI systems deployed in banking environments, where resilience and reliability are paramount.

Key Experience:

• 3-5 years Strong programming skills in Python, with experience in pandas, SQL, and ML frameworks (e.g., scikit-learn).
• Should have used Python in ML domain to build, train and maintain models.
• Familiarity with AWS services such as Lambda, Glue, CloudWatch, and Bedrock.
• Experience with container workflows (Docker) and model lifecycle management.
• Foundational knowledge of observability practices and model deployment fundamentals.
• Interest or experience in supporting AI systems used by developers or analysts.
• Strong communication and documentation skills with a collaborative team mindset.
• Ability to assume ownership of assignments and consistently meet deadlines.

Responsibilities :

• Deploy and maintain ML and GenAI models using AWS services, including SageMaker, Fargate, and Bedrock.
• Apply prompt engineering techniques to optimize GenAI model performance and reliability.
• Experience with Retrieval-Augmented Generation (RAG) applications is a plus.
• Assist in building and maintaining internal model-serving platforms to support development teams.
• Implement containerized services using Docker and deploy them to AWS infrastructure.
• Write Infrastructure-as-Code (IaC) using Terraform to automate cloud resource provisioning (nice to have ).
• Participate in unit and end-to-end testing of ML pipelines, services, and monitoring workflows.
• Support model monitoring and health tracking using AWS CloudWatch and internal observability tools.
• Document internal systems and operational processes to ensure maintainability and reproducibility.

Branch Overview

Branch delivers world-class financial services to the mobile generation. With offices in the United States, Nigeria, Kenya, and India, Branch is a for-profit socially conscious company that uses the power of data science to reduce the cost of delivering financial services in emerging markets. We believe that everyone everywhere deserves fair financial access. The rapid spread of smartphones presents an opportunity for the world’s emerging middle class to access banking options and achieve financial flexibility.

Branch’s mission-driven team is led by the founder and former CEO of Kiva.org. The company presents a rich opportunity for our team members to drive meaningful growth in rapidly evolving and changing markets. In 2019, Branch announced our Series C and garnered more than $100M in funding with investments from leading Silicon Valley firms, including Andreessen Horowitz, Trinity Capital, Foundation Capital, Visa, and the International Finance Corporation (IFC).

As a company, we are passionate about our customers, fearless in the face of barriers, and driven by data. As a product-driven org, we value bottom-up innovation and decentralized decision-making. We believe the best ideas can come from anyone in the company, and we create an environment where everyone feels empowered to propose solutions to the challenges we face.

We value diversity and are committed to providing an inclusive working environment where human beings of all backgrounds can thrive.

Job Overview

Branch launched in India in early 2019 and has seen rapid adoption and growth. We are expanding our product portfolio as well as our user base in all our markets including India. We are looking for talented Machine Learning Engineers to join us and be part of this journey. You will work closely with other Engineers, Product Managers, and underwriters to develop, improve, and deploy machine learning models and to solve other optimization problems. We make extensive use of machine learning in our credit product, where it is used (among other things) for underwriting and loan servicing decisions. We are also actively exploring other applications of Machine Learning in some of our newer products, with the ultimate goal of improving the user experience.

Machine Learning sits at the intersection of a number of different disciplines: Computer Science, Statistics, Operations Research, Data Science, and others. At Branch, we fundamentally believe that in order for Machine Learning to be impactful, it needs to be closely embedded into the rest of the product development and software engineering process, which is why we emphasize the importance of software engineering skills and experience for this role.

As a company, we are passionate about our customers, fearless in the face of barriers, and driven by data. As an engineering team, we value bottom-up innovation and decentralized decision-making. We believe the best ideas can come from anyone in the company, and we are working hard to create an environment where everyone feels empowered to propose solutions to the challenges we face. We are looking for individuals who thrive in a fast-moving, innovative, and customer-focused setting.

Responsibilities

• Credit Decisions: Core to our business is understanding and building signals from unstructured and structured data to identify good borrowers.
• Customer Service: Using machine learning and LLM/NLP, automate customer service interactions and provide context to our customer service team.
• Fraud Prevention: Identify patterns of fraudulent behavior and build models to detect and prevent these behaviors.
• Team work: Bring your experience to bear on the technical direction and abilities of the team, and work cross-functionally with policy and product teams as we improve processes and break new ground.

Qualifications

• 2+ years of hands-on experience building software in a production environment. Startup or early-stage team experience is preferred.
• Excellent software engineering and programming skills, especially Python and SQL.
• A diverse range of data skills, including experimentation, statistics, and machine learning, and have used these skills to inform business decisions.
• A deep understanding of using cloud computing infrastructure and data pipelines in production.
• Self motivation: You teach yourself new skills. You take the initiative to solve problems before they arise. You roll up your sleeves and get stuff done.
• Team motivation: You listen to others, speak your mind, and ask the right questions. You are a great collaborator and teacher.
• The drive to make a positive impact on customers' lives.

Benefits of Joining

• Mission-driven, fast-paced, and entrepreneurial environment
• Competitive salary and equity package
• A collaborative and flat company culture
• Fully-paid Group Medical Insurance and Personal Accidental Insurance
• Unlimited paid time off, including personal leave, bereavement leave, and sick leave
• Fully paid parental leave — 6 months maternity leave and 3 months paternity leave
• Monthly WFH stipend alongside a one-time home office set-up budget
• $500 Annual professional development budget
• Team meals and social events — Virtual and In-person

We’re looking for more than just qualifications -- if you’re unsure that you meet the criteria but identify with our vision of providing equal opportunity to everyone to access financial services, please do not hesitate to apply!

Branch International is an Equal Opportunity Employer. The company does not and will not discriminate in employment on any basis prohibited by applicable law.

Hi,

We are hiring for our client, a Semi conductor industry in Coimbatore location.

Machine Learning Engineer:

Experience:1-1.5 years of Experience in Developing End-to-End Machine Learning solutions for Data-driven Decision making.

Skills Required:

Strong programming proficiency in Python and related libraries (NumPy, Pandas, Scikit-learn).

Experience with machine learning frameworks (TensorFlow, PyTorch, Keras).

Solid understanding of statistical modeling, data mining, and machine learning algorithms.

Conduct exploratory data analysis, feature engineering, and model selection.

Develop and implement machine learning models using appropriate algorithms and techniques (e.g., supervised, unsupervised, reinforcement learning).

Deploy and maintain machine learning models in production environments using cloud platforms or on-premises infrastructure.

Monitor model performance, identify areas for improvement, and retrain models as needed.

Knowledge of big data technologies (Hadoop, Spark)

Experience with time series analysis and forecasting.

Conduct A/B testing and experiment with different model architectures and hyperparameters.

Proficiency in data visualization tools (Matplotlib, Seaborn, Tableau, Power BI).