Primary Responsibilities:
Engineering Leadership
- Lead multiple AI/ML engineering teams in developing scalable ML models, LLM-based solutions, and intelligent automation capabilities across business domains.
- Drive end‑to‑end delivery of AI products, from ideation through productionization, leveraging modern ML Ops practices at scale.
- Partner with cross-functional teams to determine applicability of AI to business problems
- Mentor data scientists, AI/ML scientists, and engineers to ensure the delivery of the AI/ML projects, and provide guidance on how to best use specific tools or technologies to achieve the desired results
- Build and grow a high-performing AI/ML engineering team with roles spanning ML engineers, data engineers, AI architects, and applied scientists.
Technical Leadership
- Develop and evolve machine-learning methods by adapting model architectures, learning objectives, and feature representations to healthcare-specific data, constraints, and outcomes.
- Establish evaluation frameworks for AI/ML systems beyond accuracy, including reliability, explainability, safety, and real-world impact, in partnership with clinical and business stakeholders.
- Lead failure-mode analysis and iterative method improvement based on model behavior, data drift, and production feedback.
- Define and improve GenAI and LLM-based methods, including RAG architecture, retrieval strategies, grounding techniques, prompt optimization, and hallucination mitigation.
- Establish approaches for controlling and constraining LLM behavior in regulated healthcare settings, including confidence estimation, escalation strategies, and human-in-the-loop workflows.
- Closely collaborate with the Responsible Use of AI (RUAI) team to ensure that the delivered solutions are compliant with the company policies and standards
- Implement governance aligned with responsible AI principles and regulatory frameworks (HIPAA, CMS), including automated guardrails and model observability.
- Lead value realization efforts, ensuring clear KPIs for business outcomes, quality, and operational performance.
Required Qualifications:
- Masters degree in Computer Science, Math, Statistics, or a related field and 15+ years experience OR a PhD in Computer Science or a related field and 5+ years experience
- 3+ years experience focused on AI/ML/NLP solutions delivery
- 5+ years leading AI/ML initiatives end-to-end, including method definition, evaluation, production deployment, and ongoing improvement.
- Experience in implementing AI/ML and/or NLP solutions
- Strong hands-on background in Python and AI/ML frameworks (PyTorch, TensorFlow), and ML lifecycle tooling (MLflow, AzureML, etc.).
- Demonstrated experience defining or evolving machine-learning methods, including model selection, evaluation, and iterative improvement in production systems.
- Experience developing and deploying data pipelines, machine learning models, or applications on cloud platforms (e.g., Azure, AWS, Databricks, AzureML)
- Experience with Gen AI solution pipelines (e.g., RAG) and Large Language Modeling and Transformer Architectures (e.g., BERT, GPT, etc.)
- Ability to clearly explain AI/ML methods, tradeoffs, and results to technical and non-technical stakeholders.
- Strong understanding of responsible AI, model governance, and regulatory requirements in healthcare.
- Proven understanding of mathematical foundations of machine learning, including statistics, linear algebra, and computer science
Preferred Qualifications:
- PhD in Computer Science, Mathematics, Statistics, or a related discipline
- Experience in healthcare (AI) Experience developing AI/ML systems in healthcare or other regulated industries.
- Experience working with cross-functional and distributed teams in a global and diverse environment
- Experience in establishing AI/ML best practices, standards, and ethics
- Working knowledge of Software Development tools and practices including DevOps and CI/CD tools (e.g., Git, Jenkins, Docker, Kubernetes, etc.)
- Security and vulnerability management (package scans, remediation)
- Familiarity with data versioning tools (Delta Lake, DVC, LakeFS, etc.)
- Experience with model observability tools for insights into the behavior, performance, and health of your deployed ML models (tracking, alerting, compliance monitoring, etc.)
