AI Technical Product Manager

Jobtailor • Remote (il, United States) • $140 - $200 / year • 14h ago

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, TensorFlow, PyTorch, Hugging Face, LangChain, 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.

Requirements

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/TensorFlow, LangChain, 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.

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