Description

You will design, build, and maintain end-to-end MLOps pipelines for data preparation, training, validation, packaging, and deployment.

This role is on-site.

Responsibilities

• Develop FastAPI microservices for model inference, ensuring clear API contracts, versioning, and documentation.
• Define and implement deployment strategies on AKS (blue/green, canary, shadow, champion/challenger) using GitOps with Argo CD.
• Architect and evolve a self-serve MLOps platform, establishing standards, templates, and CLI/scaffolds for repeatable delivery.
• Operationalize frameworks like scikit-learn, PyTorch, and XGBoost for low-latency, scalable serving.
• Implement CI/CD for ML workflows using GitHub Enterprise, covering testing, security scans, building, packaging, and promotion.

Required Skills

• 5+ years of professional experience in software engineering.
• Strong Python engineering skills with production experience building services using FastAPI.
• Proven MLOps experience: packaging, serving, scaling, and maintaining models as APIs.
• Hands-on CI/CD for ML using GitHub Enterprise or similar, including automated testing and release pipelines.
• Containerization and orchestration expertise with Docker and Kubernetes, specifically deploying on AKS.
• GitOps experience using Argo CD and practical knowledge of deployment strategies (blue/green, canary, rollback).
• Solid understanding of RESTful API design, microservices patterns, and API contract governance.
• Experience designing or contributing to an MLOps platform for repeatable delivery.

Key Skills

Education

Any Graduate