Machine Learning Engineer

Location: Pune / Other UST Locations

Experience: 8+

Job Type: Full-time

Grade – B2

Role Overview:

We are looking for a Machine Learning Engineer with expertise in AWS SageMaker, Python, and ML pipelines. The ideal candidate will design, build, and deploy scalable ML models, automate workflows, and optimize performance using AWS cloud infrastructure.

Key Responsibilities:

Strong (developer level) experience in python to be able to decode existing codes, re architecture (batch to stream/event based etc.) codes to make it efficient Develop, train, and deploy ML models using AWS SageMaker. Build and manage end-to-end ML pipelines, including data ingestion, transformation, training, evaluation, and deployment. Automate MLOps workflows for continuous integration and deployment (CI/CD) of ML models. Optimize model performance and cost efficiency on AWS. Collaborate with data scientists and engineers to integrate ML models into production systems. Implement best practices for model monitoring, logging, and retraining. Ensure data security, compliance, and governance in AWS environments.

Required Skills & Qualifications:

Strong experience with AWS SageMaker, AWS Lambda, Step Functions, and other AWS ML services. Proficiency in Python (developer level) and ML frameworks like TensorFlow, PyTorch, or Scikit-learn. Experience in building and automating ML pipelines (e.g., with Airflow, Kubeflow, or SageMaker Pipelines). Solid understanding of MLOps, DevOps principles, and CI/CD for ML models. Hands-on experience with AWS cloud services such as S3, EC2, Lambda, Glue, and Athena. Strong knowledge of feature engineering, model tuning, and hyperparameter optimization. Experience with containerization (Docker, Kubernetes, EKS) is a plus. Excellent problem-solving skills and the ability to work in agile teams.

Preferred Qualifications:

Experience with real-time ML model deployment and inference optimization. Experience in the Entity resolution space is preferable Knowledge of big data processing (Spark, Hadoop) and data lakes. Familiarity with Git, Terraform, CloudFormation for infrastructure as code (IaC).