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Tier-1 brand and broad ML/GenAI skillset increase competition, but senior 10+ requirement reduces applicant pool.
Healthcare data, HIPAA compliance, and clinical claims expertise strongly favor candidates with healthcare experience.
Explicit 10+ years, leadership, GenAI, HIPAA compliance, and deep technical stack make filters strict.
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Own end-to-end design, development, and production deployment of AI/ML and GenAI solutions focusing on healthcare operations and patient outcomes.
Lead architecture and implementation of scalable ML/GenAI systems including data pipelines, models, MLOps/LLMOps, and monitoring with compliance to data privacy standards (HIPAA/PHI).
Mentor a small engineering team, set engineering standards, and collaborate cross-functionally with product, data engineering, clinical, and operational stakeholders.
Bachelor's degree in Computer Science, Engineering, Math, or related field required; MS/PhD preferred or equivalent experience.
10+ years professional experience in software/ML engineering with at least 3 years in project or team leadership.
Hands-on experience in GenAI including LLMs, embeddings, RAG pipelines, fine-tuning, evaluation; proficient in Python, SQL, Spark/Dask, and workflow orchestration tools.
Cloud proficiency (AWS/Azure/GCP), containerization/orchestration (Docker/Kubernetes), CI/CD, and Infrastructure as Code (Terraform) experience required.
Experienced in delivering production-grade AI/ML systems within healthcare or similarly regulated environments with strong knowledge of data privacy and responsible AI practices.
Strong technical leadership skills shown through mentoring engineers and managing cross-functional projects involving ML pipelines, MLOps, and GenAI technologies.
Well-versed in modern ML/DL frameworks (PyTorch/TensorFlow), NLP transformers, and capable of communicating technical trade-offs to both technical and non-technical stakeholders including executives.