Backend Engineer

You will be responsible for building backend components for an MLOps platform on AWS. This includes designing and developing backend services to support feature engineering, feature serving, model deployment, and inference for both batch and real-time systems. Collaboration with global cross-functional teams and participation in an on-call rotation to handle production incidents will be required. Your core responsibilities will include backend API development, cloud-based service integration, automation, and maintaining reliable, scalable infrastructure for ML workflows. - Strong Python development experience (3+ years) - Proficiency in backend frameworks such as Flask, Django, or FastAPI - Familiarity with web servers like Gunicorn and Uvicorn - Understanding of ASGI/WSGI - Knowledge of AsyncIO and concurrent programming concepts - Experience with cloud services, especially AWS, including Lambda, API Gateway, S3, and CloudWatch - Proficiency in unit and functional testing frameworks, as well as CI/CD systems like Jenkins, GitHub Actions, or GitLab CI Nice-to-have skills: - Experience with AWS SageMaker, Kubeflow, and MLflow - Exposure to model deployment and inference pipelines - Familiarity with big data tools such as Apache Spark - Knowledge of Apache Kafka for Python client apps - Experience with DevOps tools like Docker, ECS/EKS, and Terraform - Familiarity with Python packaging tools like Wheel, PEX, and Conda environments In summary, the ideal candidate for this role is 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.,