Machine Learning Engineer

Overview

Hybrid
Depends on Experience
Contract - W2
Contract - 06 Month(s)
No Travel Required
Able to Provide Sponsorship

Skills

Accountability
Amazon SageMaker
Amazon Web Services
Artificial Intelligence
Cloud Computing
Collaboration
Communication
Computer Science
Continuous Delivery
Continuous Integration
Debugging
Docker
Good Clinical Practice
Google Cloud Platform
Kubernetes
Machine Learning (ML)
Machine Learning Operations (ML Ops)
Mathematics
Orchestration
PyTorch
Python
Research
Scalability
Software Engineering
Workflow

Job Details

Title: MLOps Engineer / Machine Learning Engineer
Location: Atlanta, GA (Hybrid – as per client need)
Job Type: Contract (W2 only)

What You’ll Do

  • Design, build, and optimise ML pipelines and production systems that train, evaluate, and serve recommendation models efficiently at scale

  • Work cross-functionally with data scientists, ML scientists, software engineers, and business stakeholders

  • Partner with ML Scientists to translate research models into well-tested, maintainable, and efficient production systems

  • Implement monitoring, observability, and retraining strategies to ensure model reliability and performance

  • Contribute to the evolution of ML infrastructure including CI/CD workflows, model registries, and feature stores

  • Diagnose and resolve production ML issues such as data inconsistencies, bottlenecks, and model drift

  • Drive engineering best practices for scalability, reproducibility, and reliability across the ML lifecycle

Minimum Requirements

  • 10+ years of relevant industry experience

  • Advanced degree in Computer Science, Mathematics, or related quantitative field

  • Strong software engineering background with clean, scalable, and maintainable coding skills (Python preferred)

  • Proven experience deploying and operating ML systems in production

  • Deep understanding of MLOps concepts: CI/CD for ML, model serving, observability, feature stores, and versioning

  • Experience with ML frameworks such as PyTorch or TensorFlow

  • Hands-on experience with orchestration tools like Airflow, Kubeflow, SageMaker, or Ray

  • Familiarity with containerization and cloud-native ecosystems (Docker, Kubernetes, AWS/Google Cloud Platform)

  • Skilled in debugging distributed ML systems and optimizing performance at scale

  • Strong communication skills, with the ability to collaborate with technical and non-technical teams

  • Passion for responsible AI, ensuring fairness, accountability, and equity in ML systems

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