Machine Learning Engineer

Job Title: Machine Learning Engineer
Location: Remote (PST Time preferred)
Duration: 06-month contract (with possible extension)
Start Date: Targeting Nov 2025
Pay Rate: $62.58/hr. on W2
Benefits: Medical, Dental, Vision.

Job Description:
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.
  

#TMN