Senior MLOps Engineer- Remote[Candidates must work PST hours]

OMG Technology•Raleigh, NC

3d•Remote

About The Position

This opportunity is for a Senior MLOps Engineer role with one of our clients. The Sr. Engineer, Machine Learning Operations, with minimal guidance, works independently and with cross‑functional partners—including biostatisticians, bioinformatics scientists, AI scientists, and software engineers—to deploy, operate, and scale machine learning solutions in production for advanced cancer screening and precision oncology applications. The role designs, builds, and maintains robust ML platforms and pipelines that ensure reliability, security, and compliance across the full model lifecycle—from data ingestion, model training, versioning and evaluation, through deployment, monitoring, and continuous improvement. This role serves as a key resource, applying in‑depth practical knowledge of ML Operations, software engineering, and cloud infrastructure to solve complex problems across multiple projects, ensuring AI/ML models are production-ready, observable, and aligned with the company's mission to help eradicate cancer.

Requirements

Bachelor’s Degree in a field related to essential duties; or Associates Degree and 2 years of relevant experience.; or High School Diploma or General Education Degree (GED) and 4 years of relevant experience.
5 years of relevant job-related experience.
Demonstrated experience with Python, at least one major ML framework (e.g., TensorFlow, PyTorch, scikit‑learn), containerization and orchestration technologies (e.g., Docker, Kubernetes), and a major cloud platform (e.g., AWS, Azure, GCP) supporting ML workloads.
Demonstrated ability to perform the essential duties of the position with or without accommodation.
Applicants must be currently authorized to work in country where work will be performed on a full or part-time basis.
We are unable to sponsor or take over sponsorship of employment visas at this time.

Nice To Haves

2+ years of life sciences industry experience working with biological data.
2+ years of industry experience in molecular diagnostics, preferably cancer diagnostics.
Expertise in data mining approaches within healthcare settings generating insight from routinely collected healthcare data.
Scientific understanding of cancer biology
Strong programming ability in Python and experience with at least one major ML framework (e.g., TensorFlow, PyTorch, scikit-learn).
Hands-on experience deploying and operating machine learning models in production, including experience with CI/CD pipelines, model packaging, and automated deployment.

Responsibilities

Designs, implements, and maintains end‑to‑end MLOps pipelines for training, validation, deployment, and monitoring of ML and AI models used in cancer screening and precision oncology solutions.
Builds and operates scalable, secure ML infrastructure on cloud and container platforms (e.g., AWS/Azure/GCP, Docker, Kubernetes) to support batch and real‑time inference workloads.
Implements CI/CD workflows for ML (data, model, and code), including automated testing, packaging, and promotion of models across development, staging, and production environments.
Establishes and manages model and data versioning, experiment tracking, and lineage to ensure reproducibility, auditability, and effective model governance.
Develops and maintains monitoring, logging, and alerting for model performance, data quality, drift, and system health, defining and meeting SLOs/SLAs for critical ML services.
Collaborates with data scientists, bioinformatics and biostatistics partners, and software/platform engineering teams to translate experimental workflows into production‑grade services integrated into customer‑facing and internal applications.
Uphold company mission and values through accountability, innovation, integrity, quality, and teamwork.
Support and comply with the company’s Quality Management System policies and procedures.
Maintain regular and reliable attendance.
Ability to act with an inclusion mindset and model these behaviors for the organization.
Ability to work on a mobile device, tablet, or in front of a computer screen and/or perform typing for approximately 90% of a typical working day.
Ability to travel 5% of working time away from work location, may include overnight/weekend travel.