Lead the product strategy and delivery for Prove AI’s Generative AI orchestration and governance platform—turning research and customer insight into secure, scalable workflows that enterprises can trust. You’ll own the end-to-end roadmap (discovery → launch → adoption), embed governance-by-design (auditability, evaluation, risk controls), and partner cross-functionally with engineering, research, compliance, and GTM to ship high-impact features that accelerate safe AI from development to production.
Job requirements:
- PM experience in AI/ML platforms, developer tools, or enterprise SaaS, with proof of end-to-end ownership at scale.
- Deep GenAI exposure in one or more areas:
- LLM data pipelines: labeling/annotation/synthetic data
- LLM applications: RAG, embeddings, vector DBs, prompt/fine-tuning.
- Validation & monitoring: observability, drift, lifecycle mgmt.
- Algorithmic auditing & risk: bias mitigation, responsible AI frameworks, regulatory alignment.
- Orchestration: hands-on product leadership around LangChain and/or Kubeflow (pipelines, workflow automation, deployment).
- Technical fluency: comfortable with APIs, evaluation frameworks, embeddings, and orchestration concepts; able to engage credibly with engineering, data, and legal/compliance stakeholders.
Nice to have:
- Familiarity with LangSmith/LangGraph (managed infra for long-running/stateful agents) and ecosystem tools like Ray, Feast, BentoML, DVC, Flyte, Dagster, or Prefect.
- Background from observability, governance, or AI platform companies or big-tech AI labs.
Job responsibilities:
- Own product strategy & roadmap for GenAI orchestration features (pipelines, agents, evaluation/observability) from market discovery through scaled rollout.
- Translate research into products: partner with ML engineers/researchers to ship LangChain/Kubeflow-backed workflows, long-running agents, and deployment tooling that solve real customer problems.
- Embed governance by design: integrate risk, auditability, bias/fairness metrics, and regulatory alignment (e.g., EU AI Act, NIST RMF, ISO/IEC) into product requirements and success measures.
- Drive evaluation & monitoring: define KPIs beyond raw accuracy—reliability, transparency, and safety—and partner on drift detection and model lifecycle management.
- Run tight execution loops: lead cross-functional squads, run experiments, validate with design partners, and iterate quickly based on signals from enterprise users (DS/ML, platform, and compliance personas).
- Go-to-market collaboration: enable sales/CS with clear positioning, demos, and documentation for developer and enterprise buyer journeys.
Benefits:
- Fully remote, work from home environment
- Flexible working hours
- Paid Time-Off
- Periodic in-person offsites globally (travel permitting)
- Long-term incentive programs
- Continued education support
- Advancement opportunity
