AI Engineer

Design and scale agent systems for planning, tool use, memory, and context management. Integrate agents with tools and data sources, implement safety guardrails and sandboxing, develop evaluation and telemetry systems, optimize reliability, and contribute to platform services and infrastructure.

Responsibilities

• Build agent capabilities for planning, tool use, memory, and context management and ship them into production.
• Integrate agents with internal and external tools and data sources using robust schemas and safeguards.
• Develop quality and evaluation systems including unit tests, regression tests, scenario benchmarks, telemetry, and automated scoring.
• Collaborate with scientists to analyze failure modes and improve performance.
• Ensure outputs are source-traceable and compliant with provenance standards.
• Implement safety measures, guardrails, and sandboxed execution for risky operations.
• Optimize performance and reliability through profiling, idempotency, retries, rate limiting, and uptime management.
• Instrument data pipelines for supervised fine-tuning and reinforcement learning.
• Contribute to agent platform services, APIs, orchestration, CI/CD, and observability.

Requirements

• Experience building production software in Python or TypeScript.
• Strong systems and API design skills, including FastAPI, gRPC, GraphQL, or similar.
• Proven experience shipping LLM applications or agentic systems.
• Familiarity with agent and orchestration frameworks and vector databases.
• Experience with cloud infrastructure, containers, Docker, Kubernetes, Terraform, CI/CD, and production telemetry.
• Ability to translate research prototypes into robust, scalable systems.
• Fine-tuning and reinforcement learning experience is nice to have.
• Familiarity with benchmarks, evaluations, schema, ontology, and provenance design is nice to have.