MLOps Engineer

Design/deploy ML pipelines and LLM-powered agents (RAG, Agentic AI) using AWS Bedrock, SageMaker, LangGraph

Develop, train, and productionize ML models (churn, recommendation, forecasting) with MLOps practices

Build RAG architectures, integrate LLM APIs, prompt engineering, and responsible AI guardrails

Strong Python (Scikit-learn, TensorFlow, PyTorch); hands-on SageMaker; vector DBs (FAISS, Pinecone, OpenSearch)

Must-have: Python, LangGraph/LangChain, Bedrock/OpenAI integration | Nice-to-have: LLM fine-tuning, LangSmith/W&B, AWS ML Specialty

Responsibilities

Design, build, and maintain scalable MLOps and DevOps solutions on AWS.

Develop and manage CI/CD pipelines for ML model deployment and application releases.

Automate infrastructure provisioning using Infrastructure as Code (IaC) tools.

Deploy, monitor, and optimize machine learning models in production environments.

Manage containerized applications using Docker and Kubernetes.

Implement model versioning, experiment tracking, and monitoring frameworks.

Ensure security, reliability, and high availability of cloud infrastructure.

Collaborate with Data Scientists, ML Engineers, and Development teams to operationalize ML workflows.

Troubleshoot deployment, performance, and infrastructure-related issues.

Technical requirements

Strong experience with AWS Cloud Services (EC2, S3, Lambda, IAM, EKS, ECS, CloudWatch, SageMaker).

Hands-on experience with MLOps tools such as MLflow, Kubeflow, SageMaker, or Vertex AI.

Experience in DevOps tools: Jenkins, GitHub Actions, GitLab CI/CD, Azure DevOps.

Proficiency in Python scripting and automation.

Expertise in Docker and Kubernetes.

Knowledge of Infrastructure as Code tools such as Terraform or CloudFormation.

Experience with monitoring tools like CloudWatch, Prometheus, and Grafana.

Strong understanding of Linux systems and networking concepts.

Experience with Git version control.

Education

Bachelor of Engineering,BTech