AI Product Manager

CTW; New York (NY); On-site; USD 120,000 - 190,000; Full time

Job summary

CTW is seeking an AI Product Manager to drive end-to-end planning and execution of GenAI and agent-based SaaS apps. You will translate complex workflows into scalable AI architectures and collaborate across engineering, data, and operations to ensure stable deployments.

The role emphasizes defining AI quality metrics, managing model tradeoffs, and prototyping multilingual demos. Strong experience with AI orchestration and rapid prototyping is required.

Qualifications

• 4+ years of experience in AI product management, technical product management, or AI solution design.
• Experience building GenAI/LLM-powered applications in production environments.
• Strong understanding of agentic workflows, tool calling, prompt engineering, RAG pipelines.

Responsibilities

• Lead end-to-end product planning and execution for GenAI and Agent-based SaaS applications.
• Collaborate with engineering, AI/ML, data, and operations teams for production deployment.
• Define evaluation frameworks for AI quality including hallucination detection and benchmarks.
• Drive AI product iteration using user feedback loops and operational metrics.

Skills

GenAI

AI product management

AI workflow design

Product strategy

Multilingual AI

Tools

Dify

Coze

LangChain

LangGraph

n8n

CrewAI

AutoGen

Job description

The AI Product Manager plays a key role in driving the success of AI-powered products by defining vision, strategy, and requirements, collaborating with cross-functional teams, and overseeing the product lifecycle from ideation to launch. This role requires a strong understanding of AI technologies and the ability to deliver consumer or enterprise products aligned with business goals.

• Lead end-to-end product planning and execution for GenAI and Agent-based SaaS applications
• Translate complex business workflows into scalable AI agent architectures and task decomposition pipelines
• Design and optimize AI workflows using orchestration platforms such as Dify, Coze, LangChain, LangGraph, n8n, or similar frameworks
• Build and validate multilingual AI agent demos and rapid prototypes for real-world operational scenarios
• Collaborate closely with engineering, AI/ML, data, and operations teams to ensure stable production deployment
• Define evaluation frameworks for AI quality, including hallucination detection, accuracy benchmarking, recall, coverage, and workflow reliability
• Design fallback and degradation strategies for AI systems under latency, token, or model constraints
• Balance response quality, inference cost, and system latency through model routing and workflow optimization
• Continuously monitor advances in LLMs, agents, AI infrastructure, and workflow automation ecosystems
• Drive AI product iteration using user feedback loops, failure analysis, and operational metrics
• 4+ years of experience in AI product management, technical product management, or AI solution design
• Experience building or managing GenAI / LLM-powered applications in production environments
• Strong understanding of Agentic workflows, tool calling, prompt engineering, RAG pipelines, and multi-model orchestration
• Familiarity with AI orchestration and automation frameworks such as Dify, Coze, LangChain, LangGraph, CrewAI, AutoGen, or similar platforms
• Ability to independently prototype workflows and collaborate effectively with engineering teams
• Strong understanding of AI system tradeoffs including:
• hallucination
• evaluation
• human-in-the-loop systems
• Ability to decompose complex business operations into structured AI workflows and deterministic + probabilistic system boundaries
• Strong communication and stakeholder management skills across technical and business teams
• Experience with multilingual AI applications and international SaaS products
• Familiarity with semantic caching, vector databases, workflow observability, or AI monitoring systems
• Experience with AI productivity tools, copilots, automation systems, or enterprise AI deployments
• Basic coding or rapid prototyping capability (Python, scripting, AI-assisted coding, API integrations, etc.)
• Understanding of MLOps, AI deployment workflows, or AI operational monitoring