
REMOTE AI/ML Engineer II
Remote · USJobSeen 1 day agoSeen in employer's feed 1 day ago
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Job overview
Insight Global seeks a Machine Learning Engineer II to build and scale a greenfield ML ecosystem for a leading healthcare organization. The role involves developing deployment pipelines, automating model lifecycles, ensuring governance, monitoring performance, and optimizing cloud infrastructure while collaborating with data scientists, engineers, and DevOps teams.
Skills & qualifications
Skills
Qualifications
Full job description
Job Description
Insight Global is seeking a Machine Learning Engineer II for a leading healthcare organization focused on advancing data-driven innovation. This candidate will help build and scale a growing machine learning ecosystem, supporting the deployment, automation, governance, and monitoring of ML models in production. The ideal candidate comes from a strong technical background in software engineering, data engineering, analytics, or machine learning and has successfully transitioned into AI/ML-focused work. This person will collaborate closely with cross-functional teams to develop scalable ML infrastructure, improve operational efficiencies, automate model lifecycle processes, and support cloud-based solutions. This is an exciting opportunity to join a greenfield environment where you can help shape machine learning operations from the ground up while working with modern technologies such as Azure Fabric, GitHub, and enterprise-scale data platforms.
Day-to-Day: Develop and refine machine learning deployment pipelines and workflows for production environments Support lifecycle governance, versioning, and reproducibility of ML models Implement and maintain model monitoring, performance tracking, and drift detection systems Build dashboards and alerting mechanisms for model performance visibility Partner with data scientists, software engineers, and DevOps teams on ML solutions Automate model retraining, validation, and deployment processes Apply healthcare data governance and security standards to ML systems Optimize and scale cloud infrastructure supporting machine learning workloads Create technical documentation and contribute to best practices and knowledge sharing Support a greenfield ML environment and help establish foundational ML operational processes
Skills and Requirements
Must-Haves: 4-5 years of hands-on AI/ML experience Background in software development, data engineering, data analytics, or similar technical discipline Strong coding/programming experience Experience supporting machine learning models in production environments Knowledge of MLOps practices, ML lifecycle management, deployment, monitoring, and automation Experience with cloud platforms and ML infrastructure optimization Familiarity with CI/CD pipelines, containerization, and cloud infrastructure Experience working in enterprise-level data environments Exposure to GitHub and Azure Fabric environments Bachelor’s degree in Computer Science, Engineering, Data Science, or related field (or equivalent experience) Plusses: Healthcare industry experience Experience in regulated environments Cloud certifications (Azure, AWS, GCP) Machine Learning or DevOps certifications Experience with VS Code Prior MLOps, DevOps, or ML Engineering experience
We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal employment opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment without regard to race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or the recruiting process, please send a request to [email protected].
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