Staff Software Engineer, Agentic Platform
Senior individual contributor architecting and scaling agentic LLM systems that turn messy manufacturing data into reliable root-cause insights. Owns orchestration, retrieval, evaluation, and guardrails for non-deterministic production systems.
About the job
Key Responsibilities
- Own technical architecture for agentic systems—orchestration, tool use, memory, retrieval, evaluation, observability, and guardrails
- Build and harden the reasoning layer and infrastructure that take customer data from ingestion through agent-driven reasoning to root-cause insight
- Establish rigorous evaluation and testing for non-deterministic systems—measuring agent accuracy against expert-validated ground truth
- Partner across Engineering and Product to shape the technical vision and roadmap for the agent platform
- Raise engineering standards through code review, design docs, and technical direction
- Mentor engineers on agentic patterns and system design
Requirements
- Minimum 7+ years of software engineering experience, with recent, hands-on focus on building agentic systems (LLM orchestration, tool/function calling, multi-step and multi-agent reasoning, retrieval and context engineering, and interoperability protocols such as MCP)
- Demonstrated ability to design and own complex distributed systems, ideally in data-intensive B2B SaaS environments
- Hands-on fluency with the modern agentic stack and strong intuition for failure modes of non-deterministic, LLM-driven systems
- Experience turning messy, heterogeneous real-world data into something a model can reason over reliably
- Track record of technical mentorship
- Strong executor in fast-paced environments who can balance shipping MVPs with production-grade systems
Nice-to-Haves / Culture Fit
- Thrives in early-stage companies
- "Data First" philosophy
- Comfortable with ambiguity and fast-moving situations
Skills
Llm Orchestration, Tool/Function Calling, Multi-Agent Reasoning, Retrieval Systems, Context Engineering, Mcp, Distributed Systems, Observability, Evaluation Frameworks, Data-Intensive Systems
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