AI Technical Product Manager

Responsibilities

• Drive technical product vision, solution architecture, and hands‑on prototyping for a major AI product area.
• Own product outcomes from discovery through pilot, scale, and ongoing optimization.
• Design and co‑develop working prototypes and production‑ready components using leading AI/ML tools and platforms (e.g., Python, Tensor Flow, PyTorch, Hugging Face, Lang Chain, Azure AI, OpenAI).
• Rapidly iterate on LLM‑based applications, conversational AI, and intelligent automation.
• Partner directly with business stakeholders to deeply understand operational challenges, map complex processes, and translate them into actionable AI use cases with clear success metrics.
• Guide AI solutions through Humana’s multi‑stage governance process—including AIRB, LRC, and Responsible AI reviews—by preparing technical documentation, scorecards, and market scans, and leading technical deep dives at each stage gate.
• Develop and apply robust evaluation frameworks to benchmark AI models, compare platform options, and ensure solutions meet business, technical, and regulatory standards.
• Author and maintain code libraries, reusable solution patterns, and technical playbooks to enable rapid, consistent AI delivery across multiple business lines.
• Provide technical mentorship to product managers, engineers, and data scientists; foster a culture of hands‑on experimentation and continuous learning.
• Communicate technical strategy, risks, and business value clearly and effectively to executive, business, and technical audiences.

• B.S. or M.S. in Computer Science, Engineering, or a related field (or equivalent experience)
• 7+ years of relevant experience, with at least 3 years in technical product management or engineering roles focused on AI/ML solutions.
• Demonstrated ability to architect, build, and scale AI applications (including LLM‑based solutions) in an enterprise environment.
• Hands‑on expertise with AI/ML tools, frameworks, and cloud platforms (Python, PyTorch/Tensor Flow, Lang Chain, vector databases, RAG architectures, prompt engineering, Azure/OpenAI, etc.).
• Experience with the full lifecycle of AI products: requirements gathering, prototyping, validation, deployment, and post‑launch optimization.
• Familiarity with enterprise AI governance, responsible AI principles, and compliance requirements (including AIRB and LRC‑style stage‑gate reviews).
• Strong communication skills, with proven ability to bridge technical and business contexts.