AI Product Engineer

What You'll Do:

Architect & Ship AI-Native Systems 

• Lead the architecture and delivery of end-to-end AI-powered systems, including agents, RAG pipelines, orchestration layers, and reasoning workflows. 

• Translate product vision into scalable technical systems. 

• Define contracts, state management strategies, and guardrails for AI-driven workflows. 

• Own and evolve API contracts that AI systems interact with, ensuring reliability, idempotency, authentication safety, and rate limiting. 

Engineer Reliability at Scale 

• Design schema enforcement and validation layers for AI-generated outputs. 

• Implement retries, fallback strategies, and failure-mode containment. 

• Establish evaluation frameworks for benchmarking, regression testing, and drift detection. 

• Create observability standards for AI systems, including structured logging, telemetry, tracing, and performance monitoring. 

• Productionize experimental AI capabilities into scalable, secure services. 

Set Direction & Elevate the Organization 

• Establish architectural patterns and standards adopted across teams. 

• Mentor engineers in AI-native and spec-driven development practices. 

• Influence engineering culture through clarity, urgency, and execution. 

• Decompose high-level business outcomes into executable technical systems. 

What You Bring:

You have mastered the fundamentals of software engineering and operate at a higher level of abstraction. You are not simply accepting AI-generated code — you review, restructure, and integrate it into cohesive, production-grade systems. 
• 4+ years building AI-augmented product capabilities (LLMs, RAG systems, agents, orchestration frameworks). 
• Event-Driven & Asynchronous Systems : Experience designing decoupled systems using queues such as Kafka, SQS, or BullMQ, and implementing asynchronous workflows that prevent blocking operations in user-facing systems. 
• State Management Strategy : Experience persisting state across sessions, managing context windows efficiently, and handling concurrency and race conditions when multiple agents interact with shared data. 
• Structured Data Enforcement : Experience enforcing structured outputs using schema validation tools such as Pydantic, Zod, or JSON Schema to ensure AI-generated outputs are reliable and machine-readable. 
• API Design & Integration : Strong understanding of REST, GraphQL, or RPC interface design, along with authentication, rate limiting, and idempotent API patterns. 

Tech Stack & Hard Skills:

Languages: Python, including asyncio, decorators, and the modern Python ecosystem. TypeScript / Node for integration and application-layer logic 

AI Stack: Orchestration frameworks such as LangChain, LangGraph, or custom agent loops. Retrieval-Augmented Generation (RAG) systems. Hybrid search, re-ranking, and chunking strategies. Vector databases such as Pinecone, pgvector, or Weaviate 

Database & Infrastructure: Advanced SQL skills including query optimization and indexing strategies. Containerization using Docker, Kubernetes or similar orchestration platforms. Experience running isolated environments for code execution