Machine Learning Engineer

We are seeking for a Machine Learning engineer on behalf of our direct clients in USA. Key Responsibilities - Design, build, and maintain end-to-end MLOps pipelines for data prep, training, validation, packaging, and deployment. - Develop FastAPI microservices for model inference with 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 (standards, templates, CLI/scaffolds) enabling repeatable, secure model delivery. - Operationalize scikit-learn and other frameworks (e.g., PyTorch, XGBoost) for low-latency, scalable serving. - Implement CI/CD for ML (test, security scan, build, package, promote) using GitHub Enterprise and related tooling. - Integrate telemetry and observability (logging, metrics, tracing) and establish SLOs for model services. - Monitor model and data drift; automate retraining, evaluation, and safe rollout/rollback workflows. - Collaborate with software engineers to integrate ML services into client applications and shared platforms. - Champion best practices for code quality, reproducibility, and governance (model registry, artifacts, approvals). Required Qualifications - Strong Python engineering skills and production experience building services with FastAPI. - Proven MLOps experience: packaging, serving, scaling, and maintaining models as APIs. - Hands-on CI/CD for ML (GitHub Enterprise or similar), including automated testing and release pipelines. - Containerization and orchestration expertise (Docker, Kubernetes) with production deployments on AKS. - GitOps experience with Argo CD; 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 (standards, templates, tooling) for repeatable delivery. - Ability to work cross-functionally with data scientists, software, and platform/SRE teams. Preferred Qualifications - Minimum 2+ years related experience - Experience with ML lifecycle tools (MLflow or similar for tracking/registry) and feature stores. - Exposure to Databricks and enterprise data/compute environments. - Cloud experience on Azure (preferred), plus GCP familiarity and managed ML services. - Familiarity with Agile practices; experience with Helm/Kustomize, secrets management, and security scanning.