- The role involves building backend components for an MLOps platform on AWS.

- You will design and develop backend services that support feature engineering, feature serving, model deployment, and inference for both batch and real-time systems.

- The job requires close collaboration with global cross-functional teams and participation in an on-call rotation to handle production incidents.

- Core responsibilities include backend API development, cloud-based service integration, automation, and maintaining reliable, scalable infrastructure for ML workflows.

Important Key Skill Set (Must-Have) :

- Backend Engineering.

- Strong Python development (3+ years).

- Backend frameworks: Flask / Django / FastAPI.

- Web servers: Gunicorn, Uvicorn.

- Understanding of ASGI/WSGI.

- Concurrency & Performance.

- AsyncIO.

- Concurrent programming concepts.

- Cloud Skills.

- Experience with AWS (preferred).

- Lambda.

- API Gateway.

- S3.

- CloudWatch.

- Testing & CI/CD.

- Unit & functional testing frameworks.

- CI/CD systems (Jenkins, GitHub Actions, GitLab CI).

Important Key Skill Set (Nice-to-Have) :

- MLOps & Data.

- Experience with AWS SageMaker, Kubeflow, MLflow.

- Exposure to model deployment & inference pipelines.

- Big data tools: Apache Spark.

- Streaming/Data Pipelines.

- Apache Kafka (Python client apps).

- DevOps.

- Docker, ECS/EKS.

- Infrastructure as Code: Terraform.

- Python Packaging.

- Wheel, PEX, Conda environments.

One-Line Summary of Ideal Candidate :

- A Python backend engineer with strong AWS experience, proficiency in FastAPI/Flask, AsyncIO, CI/CD pipelines, and exposure to MLOps tools like SageMaker or MLflow.

Skills :

- Python,docker,kubernetes,terraform,aws.