Role: Technical Product Manager(AI
/ ML)
Location: Fremont, CA (Hybrid – 3
days)
Job Description:-
- Core Product & Technical Skills
- Own product strategy and execution for AI-powered
features, from discovery → delivery → iteration
- Translate business problems into technical
requirements (PRDs, epics, user stories) for data, ML, and engineering
teams
- Strong understanding of system architecture:
APIs, microservices, event-driven systems, cloud platforms (AWS/GCP/Azure)
- Comfortable working with data pipelines, feature
stores, and model integratiOn
AI / ML Savvy (Practical, Not
Theoretical)
·
Working
knowledge of:
·
Machine
Learning fundamentals (training vs inference, model lifecycle, drift)
·
LLMs,
embeddings, RAG, fine-tuning, prompt design, agent workflows
·
Experience
defining requirements for:
·
Model
performance metrics (accuracy, latency, cost, hallucination rate)
·
Human-in-the-loop
workflows
·
Understand
AI trade-offs: cost, latency, explainability, bias, privacy, security
·
Ability
to partner with Data Scientists & ML Engineers without being one
Data & Analytics
- Strong data fluency:
- Define KPIs for AI features (adoption, quality,
trust, ROI
Execution & Delivery
- Proven ability to lead cross-functional teams
(Eng, Data, Design, Security) across India and US
- Strong backlog prioritization and roadmap
ownership
- Experience shipping v1 → vN products with
measurable outcomes
Communication & Leadership
- Excellent written and verbal communication with:
- Engineers (technical depth)
- Executives (outcomes & trade-offs)
- Customers (value & trust)
- Comfortable making decisions in ambiguity and
evolving AI landscapes
- Strong product judgment and customer empathy
Experience & Background
(Typical)
- 5–10+ years in Product Management or Technical
Product roles
- Prior experience with:
- AI/ML products, data platforms, or developer
platforms
- B2B SaaS, platform, or enterprise environments
- Hands-on experience with AI tools (OpenAI,
Anthropic, Vertex, Bedrock, etc.)
- Technical degree or strong engineering background
What Great Looks Like
- Can clearly explain what the AI is doing, why it
matters, and how it fails
- Designs products where AI augments humans, not
replaces judgment
- Thinks in systems, feedback loops, and long-term
product health
- Balances innovation speed with responsibility and
trust