Technical Product Manager

Responsibilities

• Define and execute the end-to-end product strategy, roadmap, and go-to-market plan for enterprise AI solutions.
• Design and iterate AI-powered workflows using LLMs, RAG, AI Agents, prompt engineering, and orchestration frameworks.
• Establish robust evaluation frameworks, conduct prompt iteration, and implement human-in-the-loop optimization to continuously improve model performance and user outcomes.
• Partner closely with AI engineers, ML researchers, and full-stack developers to prototype, validate, and scale AI capabilities.
• Analyze and balance critical AI trade-offs—including accuracy, latency, cost, scalability, security, and compliance—to guide production-ready decisions.
• Translate complex customer and market needs into prioritized, technically sound product requirements and detailed specifications.
• Lead cross-functional initiatives across engineering, UX, security, compliance, legal, and customer success to ensure alignment and operational readiness.
• Manage the full product lifecycle—from proof-of-concept and pilot programs through GA release, adoption tracking, and iterative refinement.
• Define and monitor product success metrics, adoption KPIs (e.g., task completion rate, time saved, engagement lift), and ROI benchmarks.
• Author clear, actionable documentation including PRDs, user stories, release notes, and internal/external AI product guides.

• Bachelor’s or Master’s degree in Computer Science, Engineering, Information Systems, or related technical field.
• Minimum 5 years of product management experience building enterprise software or SaaS products—with at least 2+ years focused specifically on AI/ML or Generative AI products.
• Proven hands‑on experience designing, shipping, and iterating AI-native products leveraging LLMs, RAG, AI Agents, or GenAI tool chains (e.g., Lang Chain, Llama Index, Vertex AI, Azure AI Studio).
• Deep understanding of prompt engineering best practices, AI evaluation methodologies (e.g., RAGAS, custom LLM eval suites), and AI product lifecycle challenges (hallucination mitigation, drift monitoring, versioning, observability).
• Demonstrated ability to collaborate effectively with AI/ML engineers, data scientists, and infrastructure teams throughout ideation, prototyping, validation, and deployment.
• Strong analytical mindset: comfortable interpreting model metrics, usage telemetry, A/B test results, and customer feedback to inform prioritization and roadmap decisions.
• Exceptional communication skills—able to distill technical AI concepts for executives, engineers, and non-technical stakeholders alike.