Software Engineer - Agentic System Development

We’re building AI into STARLIMS, a platform used across quality manufacturing, life sciences, public health, forensics, and environmental sciences.

This role is focused on agentic systems: software that reasons over a task, calls tools, works through multiple steps, and hands the result to a person to review and approve.

Our users work under strict accuracy, traceability, and validation requirements. The engineering challenge is making non-deterministic systems reliable, observable, and controllable enough to be trusted, tested, and shipped.

You’ll work on both the platform and runtime our agents execute on and the production agents built on top of it.

• 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

• Languages: TypeScript, Python
• Backend: Node.js, Python, AWS Lambda, Step Functions
• AI: OpenAI, Anthropic, MCP and related agent/tool protocols, embeddings and vector search
• Frontend: React, Next.js, Tailwind CSS
• Infrastructure: AWS, Terraform
• Testing: Jest, Playwright, pytest

• 6+ years of software engineering experience, including production systems
• 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
• Experience building production LLM systems, including tool-using or multi-step agentic workflows beyond simple prompting and chat interfaces
• 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)

Originally posted on Himalayas