Role overview
Hi
We have an immediate Openings with Our Direct Client for a Long-term contract position
**Job Title: Machine Learning**
**Location – Remote**
**Duration – 12 months**
**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.
What we're looking for
- 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.
**If interested, Please share below details with update resume:**
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