Senior AI Engineer – Core

• Own the evaluation layer for Hilbert’s production agents, including harnesses, metrics, golden datasets, regression gates, and human-in-the-loop review
• Architect and implement agent workflows using Lang Chain, Lang Graph, or equivalent frameworks
• Build state memory, routing, tool registries, and recovery paths
• Own systems from experimentation through production
• Operate production systems through tracing, monitoring, latency management, cost-per-task budgeting, and on-call
• Diagnose production failures and turn them into durable fixes and tests
• Expand agent capabilities across retrieval, orchestration, and execution
• Set technical standards, review designs, and define reusable patterns
• Collaborate with the founding team and cross-functional partners
• Communicate technical decisions, tradeoffs, and progress clearly
• Make pragmatic engineering decisions, ship, learn, and iterate
• Build intelligent retrieval combining RAG, graph-based retrieval, and other approaches
• Develop robust agentic workflows that handle edge cases, missing data, escalation, and human-in-the-loop checkpoints
• Integrate agents with external platforms and execute real-world workflows

• 6+ years of production software engineering experience
• 2+ years building LLM or agent systems used by real users in production
• Experience with APIs, services, data infrastructure, tests, CI/CD, and on-call
• Hands-on experience with Lang Chain, Lang Graph, or equivalent agent/orchestration frameworks
• Experience with agent architectures, state memory, routing, tool registries, recovery paths, and multi-step inference
• Ability to design evaluation harnesses, metrics, golden datasets, regression gates, and human-in-the-loop review
• Ability to diagnose hallucination, tool misuse, retrieval misses, and silent degradation
• Strong knowledge of retrieval-augmented generation, hybrid and graph retrieval, chunking, embeddings, ranking, and grounding
• Experience with LLM observability tools such as Langfuse or Open Telemetry
• Knowledge of cost and latency optimization
• Knowledge of MCP, tool-calling frameworks, structured output, and constrained decoding
• Clear technical communication and ability to explain architecture tradeoffs
• Ability to take ownership and work effectively in ambiguity and at startup speed
• Willingness to commute to the San Francisco office for a hybrid work schedule
• Authorization to work in the United States without visa sponsorship

Demonstrates expertise in architecting and implementing agent workflows using Lang Chain or Lang Graph, with a strong focus on production software engineering and LLM systems. Capable of optimizing performance through effective monitoring, diagnostics, and technical communication.

• Lang Chain Framework
• Lang Graph Framework
• Production Software Engineering
• LLM Systems Development
• Retrieval-Augmented Generation

• APIs
• Data Infrastructure
• CI/CD
• Agent Architectures
• State Memory
• Routing
• Tool Registries
• Recovery Paths
• Evaluation Harnesses
• Metrics

• Clear Technical Communication
• Ownership
• Ability to Work in Ambiguity

• Production Systems
• Human-in-the-Loop Review
• Cost Optimization
• Latency Management
• Hybrid Retrieval

• Langfuse
• Open Telemetry