Senior AI Technical Product Manager (AI)

TL;DR

Senior AI Technical Product Manager (LLM/RAG): Lead team of engineers, researchers, and data scientists building AI enablement and experiments with an accent on technical depth, stakeholder alignment, and quality gating. Focus on QA of AI agent outputs, reviewing RAG pipeline architectures, challenging prompt engineering, and guiding technology roadmap.

Location: Remote opportunity with occasional travel to client location in PA/New York, United States

Salary: $160 - $170 a year

Company

AI & Data Engineering company focused on data science solutions.

What you will do

• Set priorities across AI Enablement and Experiments tracks, allocate resources, and decide when to kill underperforming work.
• Liaise with internal business teams to gather requirements, translate pain points into technical work, and align with broader product direction.
• Lead operating rhythm including stand-ups, demos, planning, and progress readouts to leadership.
• Guide technology roadmap on model selection, infrastructure, build-vs-buy, and adoption of new AI tools.
• QA AI outputs, review architecture decisions for RAG pipelines and agent orchestration, and evaluate experiment designs.
• Participate in design and code reviews, optimize LLM costs, and establish AI governance practices.

Requirements

• Hands-on technical background from engineering, data science, or ML roles; moved into product from technical position.
• Strong understanding of LLM systems including RAG pipelines, prompt engineering, agent orchestration, and cost structures.
• Comfortable reading and reviewing Python code, understanding logic and architecture.
• Familiar with Azure cloud infrastructure for AI workloads, deployment, and scaling.
• 8+ years in technical product management or leading AI/ML teams.
• Experience with agile methodologies, stakeholder management, prioritization, and budget/vendor handling.

Nice to have

• Previous role as software/ML engineer or data scientist in NLP/search/content generation.
• Hands-on with RAG pipelines, AI agents, or LLM applications.
• AI evaluation frameworks, MLOps, governance, or scaling teams.