MLOps Engineer (Python Backend + AI/GenAI Experience)

We are looking for a senior MLOps Engineer with strong Python backend engineering expertise to design, build, and manage scalable ML and AI platforms. The ideal candidate has hands-on experience with AWS SageMaker, ML pipelines, Infrastructure as Code, GenAI/RAG workflows, and containerized deployments.

You will collaborate closely with Data Scientists, ML Engineers, and AI Engineers to build robust pipelines, automate workflows, deploy models at scale, and support end-to-end ML lifecycle in production.

Key Responsibilities

MLOps & ML Pipeline Engineering

• Build, maintain, and optimize ML pipelines in AWS (SageMaker, Lambda, Step Functions, ECR, S3).

• Manage model training, evaluation, versioning, deployment, and monitoring using MLOps best practices.

• Implement CI/CD for ML workflows using GitHub Actions / CodePipeline / GitLab CI.

• Set up and maintain Infrastructure as Code (IaC) using CloudFormation or Terraform.

Backend Engineering (Python)

• Design and build scalable backend services using Python (FastAPI / Flask).

• Build APIs for model inference, feature retrieval, data access, and microservices.

• Develop automation scripts, SDKs, and utilities to streamline ML workflows.

AI/GenAI & RAG Workflows (Good to Have / Nice to Have)

• Implement RAG pipelines, vector indexing, and document retrieval workflows.

• Build and deploy multi-agent systems using frameworks like LangChain, CrewAI, or Google ADK.

• Apply prompt engineering strategies for optimizing LLM behavior.

• Integrate LLMs with existing microservices and production data.

Model Deployment & Observability

• Deploy models using Docker + Kubernetes (EKS/ECS) or SageMaker endpoints.

• Implement monitoring for model drift, data drift, usage patterns, latency, and system health.

• Maintain logs, metrics, and alerts using CloudWatch, Prometheus, Grafana, or ELK.

Collaboration & Documentation

• Work directly with data scientists to support experiments, deployments, and re-platforming efforts.

• Document design decisions, architectures, and infrastructure using Confluence, GitHub Wikis, or architectural diagrams.

• Provide guidance and best practices for reproducibility, scalability, and cost optimization.