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Popular ML role, mid-level experience, metro location, and broad skillset increase applicant competition.
Healthcare-regulated ML role demands domain knowledge and compliance experience, reducing cross-industry transferability.
Explicit 5–10 years and mandatory ML frameworks, cloud, and regulated-industry experience enforce strict filtering.
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Design, develop, and implement AI/ML solutions from proof of concept through production deployment, focusing on healthcare applications.
Build and apply machine learning models including traditional ML, LLMs, generative AI, RAG, and agentic AI systems with emphasis on responsible AI principles (accuracy, fairness, explainability).
Collaborate with engineers, product managers, data scientists, and stakeholders to define project scope, success metrics, and deployment requirements, and support AI system lifecycle management including documentation and compliance.
Bachelor’s Degree or higher in Computer Science, Data Science, Engineering, Mathematics, or related field.
5-10 years of relevant experience in AI/ML engineering roles.
Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, scikit-learn, AWS Bedrock, and Azure AI Foundry.
Experience with end-to-end ML pipelines, deep neural network design and tuning, autonomous/multi-agent systems, and deployment on cloud platforms.
Experienced in building scalable, production-ready AI systems within regulated industries, preferably healthcare, with knowledge of compliance and governance.
Strong software engineering focus with ability to produce robust, testable, maintainable AI code and infrastructure.
Comfortable working in hybrid environment with 20% domestic travel and collaborating cross-functionally to translate business problems into AI-driven solutions.