AI Product Manager

New York, NY, United States (On-site)

Contract (7 months 17 days)

Published 6 hours ago

stakeholder management

AI Technologies

mlops

cost optimization

product management

cross - functional collaboration

AI/ML Platform

Google Cloud Platform (GCP)

Goal:

• Define and deliver AI products that reduce operational risk, improve reliability, lower cost, and increase productivity across large-scale production environments.

The most relevant skill:

• AI Product Management for LLM/Agentic Systems — Ability to translate production operations challenges into a measurable AI product strategy, roadmap, evaluation framework, and scalable delivery model.

Scope:

• Define product vision, strategy, roadmap, and success metrics for agentic AI solutions supporting production operations.
• Lead the end-to-end lifecycle of AI products, including discovery, prioritization, launch, evaluation, and continuous improvement.
• Own product strategy for LLMs, RAG pipelines, prompt orchestration, validation loops, and AI-powered operational workflows.
• Drive integrations with observability, incident management, deployment, and runtime ecosystems to enable diagnostics and automation.
• Establish AI safety, governance, reliability, rollback, and least-privilege access requirements with engineering, legal, and compliance teams.
• Define product KPIs/SLOs for cost, latency, reliability, and user satisfaction; prioritize optimization initiatives.

Must have (Non negotiable):

• Product Management Leadership — 10–15 years overall experience with significant product management ownership of complex technical products.
• Technical Degree — Bachelor’s degree in Engineering, Computer Science, or equivalent qualification.
• AI/ML Product Experience — Proven leadership of large multi-team AI/ML or cloud-based product initiatives; able to align business and technical priorities.
• LLMs and Agentic Architectures — Deep understanding of LLM capabilities, agent workflows, orchestration patterns, limitations, and evaluation methods.
• RAG Pipelines — Define requirements for retrieval systems, knowledge grounding, data quality, validation, and continuous knowledge refresh.
• MLOps and AI Lifecycle — Understand model evaluation, deployment, monitoring, experimentation, governance, and production operations.
• Technical Stakeholder Management — Demonstrated ability to work with engineering teams, product managers, and end users to define roadmaps and requirements.
• Product Discovery & Requirements — Convert user pain points into clear requirements, user stories, prioritization frameworks, and measurable outcomes.
• AI Safety & Governance — Define policies, deterministic fallback mechanisms, auditability, rollback strategies, and responsible AI controls.
• Metrics-Driven Product Management — Define and track KPIs/SLOs for latency, cost, reliability, adoption, and user satisfaction.
• Cloud Platform Knowledge — Strong understanding of large-scale cloud ecosystems and operational environments (AWS, Google Cloud, or equivalent).
• Executive Communication — Communicate complex technical strategy effectively to senior technical and non-technical stakeholders.

Good to have (Negotiable):

• Observability Platforms — Experience integrating AI products with monitoring and telemetry ecosystems.
• Incident Management Systems — Knowledge of operational tooling used for incident response and post-incident analysis.
• Prompt Engineering — Understanding of prompt optimization techniques for quality, reliability, and cost efficiency.
• Model Routing and Caching — Experience defining product requirements for performance and cost optimization patterns.
• Production Support Domain Knowledge — Familiarity with large-scale enterprise production operations and reliability engineering workflows.
• AI Evaluation Frameworks — Experience creating business-aligned benchmarks and testing methodologies for foundation models.

Rejection list (Disqualifiers):

• Reject if missing: Significant product management experience leading complex technical products.
• Reject if missing: Strong AI/ML product experience involving LLMs, agentic systems, or related technologies.
• Reject if missing: Deep understanding of RAG pipelines, MLOps concepts, and AI development lifecycle.
• Reject if missing: Ability to define product strategy, roadmaps, metrics, and stakeholder alignment.
• Reject if missing: Experience collaborating effectively with engineering and cross-fun…