Role Overview

Own Ascend's GenAI & Agentic AI Platform — shared infrastructure enabling product teams to build, deploy, and govern LLM-powered features and AI agents at scale. You'll define platform strategy across LLM gateway, RAG pipelines, agent orchestration, prompt versioning, and AI observability.

Key Responsibilities:

Platform Strategy & Roadmap

Own the multi-year roadmap: LLM gateway, RAG, agent orchestration, prompt management, and AI guardrails.
Translate business and engineering needs into a prioritised backlog with clear metrics (time-to-production, developer NPS, latency, cost per inference).
Make build-vs-buy decisions on LLM providers, vector stores, agent frameworks, and eval tooling.
Track the GenAI landscape and assess emerging models and frameworks (LangChain, LlamaIndex, AutoGen).

Developer Experience & Adoption

Define APIs, SDKs, and self-serve tooling so squads can integrate LLMs and deploy AI agents without rebuilding boilerplate.
Drive platform adoption; track onboarded teams, deployed features, agent usage, and reliability KPIs.
Maintain a developer portal with documentation, prompt libraries, and best-practice guides.

AI Feature Delivery

Write sharp PRDs and user stories defining model inputs, outputs, fallback states, and edge cases.
Set standards for agent memory, tool use, task decomposition, and human-in-the-loop escalation.
Establish reliability and eval frameworks for agentic workflows, including failure detection and rollback.

Governance, Safety & Compliance

Embed responsible-AI controls: content moderation, hallucination detection, prompt injection protection, and audit logging.
Ensure all GenAI capabilities meet BOT, PDPA, and AMLO requirements (data residency, consent, explainability).
Own incident response for model degradation, cost overruns, and safety events.

Cross-Functional Leadership

Align squads across payments, lending, insurance, and customer service on GenAI platform use.
Coordinate ML engineering, data engineering, security, legal, and business stakeholders.
Present platform strategy, capability progress, and ROI to VP/C-suite.

Qualifications:

Required

5+ years in product management; 2+ years owning developer-facing or platform/infrastructure products.
Hands-on familiarity with LLMs, RAG architectures, and prompt engineering — can assess output quality and hold technical conversations with ML engineers.
Experience shipping products built on LLM APIs (OpenAI, Anthropic, Google Gemini, or open-source models).
Strong analytical skills; comfortable with SQL, eval benchmarks, and cost-latency-quality trade-offs.
Excellent written and verbal English; able to write crisp PRDs, strategy memos, and stakeholder decks.

Preferred

Exposure to agentic AI frameworks: LangChain Agents, AutoGen, CrewAI, or similar.
Experience with vector databases (Pinecone, Weaviate, pgvector) and RAG pipeline design.
LLM evaluation and red-teaming / safety testing experience.

Product Manager (AI Platform)