We are looking for a seasoned Sr Product Manager who sits at the intersection of product strategy, artificial intelligence, and hands-on engineering collaboration. You will own the product vision for AI-powered features, partner closely with forward-deployed engineering teams at customer sites, and leverage modern AI coding tools to rapidly prototype, validate, and ship solutions. If you move fluidly between product roadmaps and terminal windows, thrive in ambiguity, and believe that the best PMs ship working software — this role is for you.
Key Responsibilities:
Product Strategy & Roadmap
▸ Define and own the product roadmap for AI-driven features, aligning cross-functional stakeholders on priorities and outcomes.
▸ Translate customer pain points — gathered during FDE engagements — into well-scoped product requirements and acceptance criteria.
▸ Partner with engineering and design to run rapid discovery sprints and deliver MVPs within compressed timelines.
▸ Own the product P&L narrative: track adoption, NPS, churn, and revenue impact of AI features.
Forward Deployed Engineering (FDE) Collaboration
▸ Embed with customer teams in FDE capacity to understand real-world workflows, data constraints, and integration environments.
▸ Act as the product-to-engineering bridge on-site, translating live customer feedback into actionable specs in real time.
▸ Use AI coding tools (Claude, Cursor, GitHub Copilot, OpenAI Codex) to build proof-of-concept prototypes that demonstrate value before full engineering investment.
▸ Lead technical workshops and demos for enterprise stakeholders, articulating both the 'what' and the 'how' of AI capabilities.
AI-Powered Code Generation & Tooling
▸ Independently generate, review, and iterate on code using AI assistants to accelerate feature validation and internal tooling.
▸ Evaluate emerging AI models and developer tools; recommend adoption or deprecation based on productivity and quality benchmarks.
▸ Champion responsible for AI practices — including prompt engineering standards, output validation, and bias/risk reviews — across the product org.
▸ Maintain working knowledge of LLM APIs (Anthropic, OpenAI) including token limits, context windows, fine-tuning options, and safety guardrails.
Cross-Functional Leadership
▸ Drive alignment across design, data science, platform engineering, and GTM teams throughout the product lifecycle.
▸ Mentor junior PMs and Associate PMs on AI tooling best practices and FDE methodologies.
▸ Represent the product voice in executive reviews, customer advisory boards, and external conferences.
Required Qualifications:
▸ 7 – 9 years of product management experience, with at least 3 years directly owning AI/ML-enabled products.
▸ Demonstrated Forward Deployed Engineer (FDE) experience
▸ Proven ability to write, review, and ship production-quality code snippets or automation scripts using AI coding assistants.
▸ Hands-on proficiency with:
– Claude (Anthropic) – prompt engineering, API integration, multi-turn conversation design
– Cursor – AI-powered IDE for rapid code generation and refactoring
– GitHub Copilot – inline code suggestions, test generation, documentation automation
– OpenAI Codex – code synthesis, natural language to code workflows, API usage
▸ Strong grasp of software development fundamentals: REST APIs, data pipelines, SQL/NoSQL, cloud platforms (AWS / GCP / Azure).
▸ Experience with agile/scrum delivery; comfort with tools like Jira, Linear, Notion, and Confluence.
▸ Excellent written and verbal communication skills — able to explain complex AI concepts to non-technical audiences.
▸ Bachelor's degree in Computer Science, Engineering, or related field (or equivalent practical experience).
Preferred Qualifications:
▸ MBA or advanced degree in a technical discipline.
▸ Familiarity with LLM fine-tuning, RAG (Retrieval-Augmented Generation) architectures, or vector database ecosystems (Pinecone, Weaviate, pgvector).
▸ Experience in SaaS, EdTech, FinTech, or Enterprise Software verticals.
▸ Open-source contributions or personal AI/ML projects on GitHub.
▸ Prior experience as a Software Engineer, ML Engineer, or Solutions Engineer before transitioning to product.
▸ Familiarity with evaluation frameworks for LLM outputs (BLEU, ROUGE, human eval pipelines).
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