Lifesight - Product Lead

About :

Were an AI studio within Lifesight that rapidly builds, launches, and scales AI native products. We operate in focused weeks long build cycles to ship MVPs, validate value, and iterate quickly.

Role Summary :

Youll be the product lead in a compact, high output pod (Senior Fullstack AI Engineer, Junior Fullstack AI Engineer, Marketer, Ops). You will shape the problem, define the value hypothesis, and co build prototypes yourself (prompting, lightweight coding, RAG scaffolding) to de risk early, then drive an MVP to production in weeks. Youre equal parts PM, experiment designer, and AI tinkerer.

What Youll Do :

Discovery & Strategy :

- Frame problems, jobs to be done, and value hypotheses; define target user, must win use cases, and success metrics.

- Map incumbents/workarounds; identify where AI gives a 10 edge (personalization, automation, prediction, UX).

- Prioritize ruthlessly for a 6 week MVP; maintain a lean roadmap and cut scope aggressively to hit outcomes.

Hands On AI Prototyping :

- Build quick experiments yourself (prompt/system instruction design, function/tool calling, structured outputs).

- Stand up lightweight scripts/notebooks or small services to test feasibility (Python/TypeScript + LangChain/LlamaIndex).

- Assemble RAG spikes : data scoping, chunking strategy, embeddings, vector search, context windows, eval sets.

- Create prompt/eval repos; run side by side model tests; instrument quality/latency/cost telemetry.

Product Execution :

- Write crisp specs : user stories, acceptance criteria, data contracts, analytics events, safety/guardrail requirements.

- Drive design with low fi wireframes and clickable prototypes (Figma); specify UX for streaming and error states.

- Partner with engineers to sequence delivery; unblock daily; manage risk via spikes, flags, and phased rollouts.

- Ensure observability from day 1 (events taxonomy, funnels, cohorting); define experiment plans (A/Bs) and launch checklists.

AI Quality, Safety & Compliance

- Define guardrails and evaluation protocols (red team prompts, refusal/citation policies, safety filters, fallbacks).

- Track AI quality signals (accuracy, coverage, helpfulness, CSAT); iterate prompts/models and retrieval strategy.

- Coordinate privacy/security reviews; ensure PII handling, data retention, and vendor usage meet studio standards.

Go to Market & Outcomes

- Craft positioning and messaging with the marketer; align activation/onboarding to the AI value moments.

- Set adoption/engagement targets (activation %, WAU, task success, LLM cost per task) and run weekly metric reviews.

- Close the loop with qualitative feedback (surveys, interviews, support/chat logs) to inform the next iteration.

Minimum Qualifications

- 4 - 8 years in product management delivering 0 1 or growth iterations; startup or studio experience preferred.

- Hands on with AI : can design prompts/system instructions, wire up function/tool calling, and build quick RAG spikes.

- Working proficiency in Python. Nextjs or TypeScript for prototyping; comfortable with APIs/SDKs and basic CLI/git.

- Strong fluency with product analytics (PostHog/Mixpanel/Amplitude), events taxonomies, and experiment design.

- Experience specifying and shipping AI powered UX (streaming, uncertainty, fallbacks, human in the loop).

- Excellent written specs, prioritization, and cross functional facilitation; bias to ship and learn.

Nice to Have

- Prior experience in SaaS creation tools, or workflow automation products.

- Knowledge of vector search (pgvector/Pinecone/Weaviate), embeddings, and retrieval strategies.

- Prompt/model evaluation tooling (Langfuse/Humanloop), LLM observability (Helicone), and OpenTelemetry.

- SQL for self serve analysis; dashboarding (Metabase/Looker); familiarity with data privacy frameworks.

- Light design skills (Figma) and comfort running user interviews and usability tests.