Senior AI Engineer
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Job Description
Senior AI Engineer building reliable, observable agentic systems for STARLIMS laboratory software. Developing runtimes, production agents, evaluation harnesses, safeguards, and enterprise integrations.
Responsibilities:
- Design and build the runtime for agent planning, execution loops, tool calling, state management, durable execution, and failure recovery
- Build safe access layers for platform data and external systems
- Design agent and workflow coordination, delegation, and handoffs
- Make agent behavior versionable, testable, measurable, and regression-safe
- Build reusable primitives for configuring new agents
- Convert domain workflows into working agents with defined goals, actions, execution flows, failure handling, and success criteria
- Ground agent decisions and outputs in authoritative enterprise data
- Implement human-in-the-loop approval gates, override capture, uncertainty handling, and evidence for decisions
- Use user corrections and overrides to improve agents
- Build evaluation harnesses for multi-step behavior and define production quality, reliability, latency, cost, and intervention metrics
- Implement guardrails, fallbacks, timeouts, cost ceilings, observability, and tracing
- Design safeguards against prompt injection, unsafe tool use, excessive permissions, data leakage, and other agent security risks
- Manage prompt evolution, model drift, and non-determinism across releases
- Integrate agents with platform APIs and third-party enterprise systems
- Build retrieval and context pipelines for reliable, permission-aware enterprise data
- Design controlled automated execution paths with traceable audit trails
- Build and operate AWS backend services
- Own significant system architecture and contribute to technical decisions
- Contribute to infrastructure-as-code and deployment pipelines
Requirements:
- 6+ years of software engineering experience, including production systems
- Experience building production LLM systems with tool-using or multi-step agentic workflows
- Strong understanding of LLM behavior, limitations, and failure modes
- Experience with LLM APIs, tool and function calling, and planning and execution loops
- Experience evaluating and debugging non-deterministic systems
- Solid backend and cloud experience with AWS or equivalent
- Proficiency in TypeScript and/or Python
- Comfortable debugging distributed and non-deterministic systems
- Comfortable trading off accuracy, latency, reliability, and cost
- Comfortable working in ambiguous problem spaces
- Comfortable owning production systems end-to-end
- Comfortable choosing conventional software when AI is not appropriate
- Nice to have: C# and Microsoft .NET Framework
- Nice to have: MCP and related agent/tool protocols
- Nice to have: agentic evaluation pipelines and metrics
- Nice to have: regulated or domain-heavy systems experience
- Nice to have: retrieval and grounding techniques
- Nice to have: workflow and durable-execution platforms such as Temporal, Step Functions, or n8n
- Nice to have: containerization and orchestration with ECS, EKS, or Kubernetes
- Nice to have: infrastructure as code with Terraform or similar
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