Stellar Consulting Solutions, LLC
Machine Learning Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for a Machine Learning Engineer on a 6-month remote contract, requiring strong Python and FastAPI skills, MLOps experience, CI/CD knowledge, and expertise in Docker and Kubernetes. Applicants must be based in the US.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
November 8, 2025
🕒 - Duration
More than 6 months
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🏝️ - Location
Remote
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
United States
-
🧠 - Skills detailed
#Observability #Docker #Automated Testing #Deployment #FastAPI #Python #Data Science #API (Application Programming Interface) #Scala #Logging #Microservices #Documentation #ML (Machine Learning) #CLI (Command-Line Interface) #Argo #Security #PyTorch #GitHub #Kubernetes
Role description
Machine Learning Engineer
Contract Duration: 6 Months (Possible Extension)
Location: Remote
Only for applicants currently in US.
Job Description:
- 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).
Must Haves:
- 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.
Machine Learning Engineer
Contract Duration: 6 Months (Possible Extension)
Location: Remote
Only for applicants currently in US.
Job Description:
- 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).
Must Haves:
- 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.
