Machine Learning Engineer

**Job Title:**
Machine Learning Engineer

**Location:**
Remote USA

**Duration:**
06-month contract (with possible extension)

**Start Date:**
Targeting Nov 2025

**Pay Rate:**
$62.58/hr. on W2

**Benefits:**
Medical, Dental, Vision.

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**Note:**
Only W2 candidates will be considered. No C2C applicants, please.

**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.

**🤖
*Passionate about taking machine learning models from notebooks to production? Join us to design world-class MLOps pipelines and deploy intelligent solutions on AKS!***