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# Agentic AI Engineer - Core Product

- **Company:** Multiscale Technologies, Inc.
- **Location:** San Francisco, CA, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, Application Programming Interfaces (APIs), Amazon Web Services, Audit Trail, Microsoft Azure, Cyber Security, Graph Database, Automation of Marketing, Neo4j, Open Source Technology, Role-Based Access Control, Retrieval-Augmented Generation, Uncertainty Quantification (UQ), Large Language Models, Multi-Agent Systems, Agentic-AI, Api Management, Human in the Loop, Docker
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25303_44068e61bc62b3faff59bc4247a77528

## About the Role

* 5+ years of production software engineering, including 2+ years shipping LLM or agentic systems; Docker and Kubernetes are second nature on Azure, AWS, or GCP, * Programmatic prompt optimization (DSPy or similar)
 * Uncertainty quantification or model calibration work
 * Graph databases in production
 * Network-isolated or regulated-environment deployment experience
 * Any exposure to semiconductor manufacturing, fab operations, or materials science
 * Open-source agentic work - show us the system you operated longest, not the flashiest demo

## Description

MIND, our agentic platform, powers autonomous agents that design experiments, optimize manufacturing steps, and accelerate scientific discovery. This team has no shortage of ideas - what this seat owns is making them survive production. You'll design the agent harness and evaluation infrastructure from first principles, operate it at scale, and still be running it a year from now. Releases here are human-gated by design: you'll build the machinery that makes an agent's work trustworthy enough for a process engineer to sign off on. You'll partner with our security architecture lead on RBAC, audit trails, and network-isolated serving for air-gapped customer environments - that's a dedicated seat, not this one., * Multi-agent orchestration on LangGraph - sequential, loop, parallel, and supervisor patterns chosen deliberately, deterministic and observable, built to survive real production conditions

 * The operational backbone of long-running agents: workers, queues, timeouts, retries, checkpointing, backpressure, and failure domains
 * Evaluation infrastructure as a first-class system - LLM-as-judge gating, golden datasets, replay runs, and full-stack tracing
 * Human-in-the-loop machinery: checkpoints, review tooling, and judge-gated flows that decide what reaches a person
 * MCP servers and tool-registration APIs that give agents persistent context and shared state across complex workflows
 * Knowledge-graph-backed agent context (Neo4j) and RAG pipelines tuned for materials-science and fab retrieval
 * The API contracts with the core Multiscale platform - the foundation everything else depends on, * You've gated something real on evaluation - judge thresholds, golden-set regressions, replay runs - and evals show up in your answers before we ask