Note: The job is a remote job and is open to candidates in USA. STARLIMS is building AI into a platform used across quality manufacturing, life sciences, public health, forensics, and environmental sciences. The Senior AI Engineer will build agent platforms, runtimes, and production agents, with a focus on reliability, evaluation, observability, secure integrations, and human oversight in regulated environments.
Responsibilities
- Design and build the runtime our agents execute on: planning and execution loops, tool calling, state management, durable execution, and failure recovery
- Build the layer through which agents reach platform data and external systems safely
- Design coordination, delegation, and handoff across agents and workflows where needed
- Make agent behavior versionable, testable, measurable, and regression-safe across releases
- Build reusable primitives so new agents are configured rather than rebuilt from scratch
- Take a domain workflow from expert conversation to a working agent: goals, actions, execution flow, failure handling, and success criteria
- Ground agent decisions and outputs in authoritative enterprise data rather than relying on model knowledge alone
- Implement human-in-the-loop by design, including approval gates, override capture, uncertainty handling, and clear evidence for agent decisions. Agents recommend and draft; people decide
- Close the loop: turn user corrections and overrides into signals that measurably improve the agent
- Build evaluation harnesses for multi-step behavior, not single-response accuracy: task completion, tool-call correctness, groundedness, trajectory quality, and regression across model, prompt, and tool changes
- Define production metrics for agent quality, reliability, latency, cost, and human intervention rates
- Implement guardrails, fallbacks, timeouts, cost ceilings, and end-to-end observability and tracing across agent runs
- Design safeguards against prompt injection, unsafe tool use, excessive permissions, data leakage, and other agent-specific security risks
- Manage prompt evolution, model drift, and non-determinism while maintaining consistent, measurable system behavior across releases
- Integrate agents with platform APIs and third-party enterprise systems already running in our customers’ environments
- Build retrieval and context pipelines that turn fragmented enterprise data into reliable, permission-aware agent context
- Design controlled execution paths for automated actions, with a complete, traceable audit trail
- Build and operate backend services on AWS (Lambda, API Gateway, DynamoDB, Step Functions, etc.)
- Own significant parts of the system architecture and contribute to key technical decisions
- Contribute to infrastructure-as-code and deployment pipelines
Skills
- * 6+ years of software engineering experience, including production systems
- * Experience building production LLM systems, including tool-using or multi-step agentic workflows beyond simple prompting and chat interfaces
- * Strong understanding of LLM behavior, limitations, and failure modes, especially how errors compound across a multi-step run
- * Experience with LLM APIs, tool and function calling, and designing planning and execution loops
- * Experience evaluating and debugging non-deterministic systems
- * Solid backend and cloud experience (AWS or equivalent)
- * Proficiency in TypeScript and/or Python
- * Debugging across distributed and non-deterministic systems
- * Making explicit tradeoffs between accuracy, latency, reliability, and cost
- * Working in ambiguous problem spaces where the right architecture isn't obvious yet
- * Owning production systems end-to-end
- * Choosing conventional software over AI when AI isn't the right solution
- * C#, Microsoft .NET Framework
- * Tool and interop protocols such as MCP
- * Evaluation pipelines and metrics built specifically for agentic systems
- * Experience in regulated or domain-heavy systems (validation, audit trails, controlled change)
- * Retrieval and grounding techniques for supplying agent context
- * Workflow and durable-execution platforms (Temporal, Step Functions, n8n, etc.)
- * Containerization and orchestration (ECS, EKS, Kubernetes)
- * Infrastructure as Code (Terraform or similar)
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