AI/ML Engineer – Mlops (AWS)

AI/ML Engineer – MLOps (AWS)

Company Name: Confidential

Job Title: AI/ML Engineer – MLOps (AWS)

Qualification: Any Graduation

Experience: 4+ years

Must Have Skills: Strong hands-on experience with AWS ML ecosystem, including Amazon SageMaker, Lambda, Step Functions, ECS/EKS, and CloudFormation. Proficiency in Python and key ML libraries such as TensorFlow, PyTorch, scikit-learn, Pandas, and NumPy. Experience implementing MLOps pipelines — model training, versioning, CI/CD, deployment, and monitoring. Knowledge of containerization and orchestration tools like Docker and Kubernetes (EKS preferred). Strong understanding of DevOps and automation tools (Git, Jenkins, Terraform, AWS CodePipeline).

Good to Have Skills: Experience with AWS security best practices, IAM policies, and role-based access management for ML workflows. Knowledge of data engineering tools — AWS Glue, Kinesis, Redshift, or EMR for data ingestion and processing. Familiarity with MLOps frameworks such as MLflow, Kubeflow, DVC, or Airflow. Exposure to model governance, monitoring, and explainability tools (e.g., SageMaker Clarify, Evidently AI). Experience with LLM deployment, fine-tuning, and Generative AI integration on AWS.

Roles and Responsibilities: Design, build, and maintain end-to-end MLOps pipelines on AWS for model training, testing, and deployment. Collaborate with data scientists to productionize machine learning models with robust CI/CD pipelines. Implement model monitoring, drift detection, retraining automation, and logging using AWS tools. Manage and optimize cloud infrastructure costs, scalability, and reliability for ML workloads. Integrate data pipelines and ensure smooth data flow between storage, preprocessing, and model services. Build API endpoints and model-serving layers using AWS Lambda, ECS, or SageMaker Endpoints. Ensure security, compliance, and governance in all stages of the ML lifecycle. Troubleshoot and resolve deployment and performance issues in production environments. Collaborate with cross-functional teams to improve automation, observability, and continuous delivery. Stay current with the latest AWS MLOps tools, frameworks, and AI trends to drive innovation.

Location: Bangalore, Pune, and Hyderabad

CTC Range: 17 LPA

Notice Period: Immediate

Shift Timings: General

Mode of Interview: Virtual

Mode of Work: Hybrid

Mode of Hire: Contract