MLOps Engineer

  • Posted 4 hours ago | Updated 4 hours ago

Overview

Remote
Depends on Experience
Contract - W2
Contract - 6 Month(s)

Skills

PyTorch
TensorFlow
AWS Sagemaker
Azure ML
GCP Vertex AI
Airflow
Kubeflow
MLflow
A/B testing
Machine Learning (ML)
Kubernetes
Artificial Intelligence
Continuous Delivery
Continuous Integration
Deep Learning
Good Clinical Practice
Health Care
High Availability

Job Details

Genzeon, an AI and automation company with deep engineering and data expertise, dedicated to serving the healthcare and retail industries. Our platform solutions including HIP One, CompliancePro Solutions, and Patient Engagement Solutions empower organizations to scale innovation and transform outcomes.

Genzeon is a global community of innovators and problem-solvers, with a culture built on inclusion, flexibility, and purpose-driven work. With four global delivery centers, we support providers, payers, Healthtech, and retail organizations worldwide.

Genzeon has an exciting opening for MLOps Engineer to join our dynamic team.

MLOps Engineer

Contract

Remote (USA)

Job Description

We are seeking an MLOps Engineer to build and support scalable infrastructure for training, deploying, and monitoring machine learning models. The role will focus on optimizing model training efficiency, automating deployment workflows, ensuring robust observability, and maintaining high-availability production systems.

Key Responsibilities

  • Set up and manage scalable ML training environments using AWS, Azure ML, or similar cloud platforms
  • Implement checkpointing, experiment tracking, logging, and real-time monitoring for ML workloads
  • Optimize model performance through quantization-aware training, memory-efficiency techniques, and hardware utilization best practices
  • Automate CI/CD pipelines for ML models, including deployment, versioning, rollback, and A/B testing
  • Collaborate with data scientists and engineers to streamline workflows from experimentation to production

Required Skills & Experience

  • Hands-on experience with cloud ML platforms (AWS Sagemaker, Azure ML, Google Cloud Platform Vertex AI)
  • Strong understanding of containerization, Kubernetes, and ML workflow orchestration (Airflow, Kubeflow, MLflow)
  • Experience with model optimization (quantization, pruning, performance tuning)
  • Proficiency with CI/CD tools, infrastructure-as-code, and automation frameworks
  • Solid programming skills in Python and familiarity with deep learning frameworks (PyTorch, TensorFlow)
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