Description

You will own the product strategy and lifecycle for agentic AI systems, defining roadmaps that balance reliability, risk reduction, and cost efficiency.

This role is hybrid.

Responsibilities

• Define product vision and strategy for LLM-based applications, translating user pain points into clear requirements and objective functions.
• Lead the end-to-end product lifecycle, establishing evaluation frameworks for foundational and open-source LLMs to meet production needs.
• Own the RAG pipeline strategy, defining data quality requirements, domain knowledge scope, and feedback loops for knowledge freshness.
• Drive integration with runtime ecosystems for observability and incident management, enabling automated diagnostics and intelligent post-incident analysis.
• Establish AI safety, reliability, and governance frameworks, including deterministic fallbacks and rollback strategies in collaboration with legal and compliance.

Required Skills

• 10+ years of experience with a significant portion in product management for AI/ML or technical cloud-based products.
• Deep understanding of the AI/ML development lifecycle, including LLMs, agentic architectures, RAG pipelines, and MLOps.
• Experience with AWS or Google Cloud environments.
• Ability to engage in deep technical discussions with engineering teams without requiring hands-on coding proficiency.
• Proven track record of leading large, multi-team product initiatives in fast-paced, changing environments.
• Strong analytical skills for defining product metrics, SLOs, and KPIs to drive data-informed decisions.
• Excellent oral and written communication skills, with the ability to articulate complex technical visions to senior leadership.

Preferred Skills

• Experience defining business-facing evaluation frameworks for open-source LLMs.
• Knowledge of prompt engineering, caching, and model routing techniques for performance optimization.

Key Skills

Education

Any Graduate