Machine Learning Engineer

Overview

Remote
Depends on Experience
Contract - W2

Skills

Machine Learning
Python
FastAPI
MLOps

Job Details

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.

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 Google Cloud Platform familiarity and managed ML services.

- Familiarity with Agile practices; experience with Helm/Kustomize, secrets management, and security scanning.

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