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
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
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

4+ years building AI-augmented product capabilities (LLMs, RAG systems, agents, orchestration frameworks)
Experience designing decoupled systems using queues such as Kafka, SQS, or BullMQ
Experience persisting state across sessions, managing context windows efficiently, and handling concurrency and race conditions
Experience enforcing structured outputs using schema validation tools such as Pydantic, Zod, or JSON Schema
Strong understanding of REST, GraphQL, or RPC interface design, along with authentication, rate limiting, and idempotent API patterns

AI Engineer

Key skills