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Senior Technical Leader — Agentic AI (Engineering Tools)

Posted 9 Days Ago
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In-Office
San Francisco, CA, USA
Senior level
In-Office
San Francisco, CA, USA
Senior level
Leads development of agentic AI capabilities that execute multi-step workflows across desktop engineering tools. Owns agent architecture, evaluation frameworks, benchmarking, cost efficiency, user-story validation, and production delivery. Serves as player-coach for a small AI engineering team, writing code, reviewing designs, and setting technical direction. Requires expertise in LLM application architecture, real-world agents, Python, desktop automation, engineering software domains, and enterprise deployment constraints.
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Senior Technical Leader — Agentic AI (Engineering Tools)
San Francisco, CA, on-site · Full-time · No sponsorship

The build: Our client is hiring a senior technical leader to own the core agent intelligence that turns mechanical engineers' intent into reliable, cost-efficient multi-step workflows across desktop engineering tools. This role sits at the intersection of applied agentic AI, user research, and product delivery — and will determine the product's real-world value to enterprise customers.

What you'll do

  • Lead development of the core agent intelligence layer that executes multi-step workflows across complex desktop engineering software.
  • Report to the CTO and serve as technical lead for a small team of AI engineers, a user researcher, and domain expert contractors.
  • Own the full product loop: define agent capabilities from user stories, build implementations, and benchmark against real workflows.
  • Drive agent task success rate by defining evaluation frameworks, establishing baselines, and iterating to improve completion metrics.
  • Set and enforce per-task token budgets and track cost per completed workflow to ensure commercial viability.
  • Build rigorous, reproducible evaluation infrastructure grounded in validated user stories.
  • Lead user story mapping and validation through interviews and close collaboration with domain experts.
  • Translate validated user stories into testable evals and close the loop between research and benchmarking.
  • Own agent architecture decisions including tool-calling, state management, error recovery, model routing, and context management.
  • Act as a player-coach: write production code, review designs, unblock the team, raise engineering standards.
  • Collaborate cross-functionally with integrations, product, and customers during POCs.


Requirements

What we're looking for

  • 7+ years software engineering, including at least 2 years building agentic LLM-based agents that act in the real world.
  • Deep experience designing LLM application architectures: model selection, context/window management, retrieval, orchestration patterns.
  • Proven ability to build evaluation and benchmarking frameworks measuring task completion, cost efficiency, and failure modes.
  • Technical leadership experience setting direction for small teams (3–6 engineers) and performing meaningful code review.
  • Strong Python skills and familiarity with LLM tooling (function calling, tool APIs, observability/tracing, evaluation frameworks).
  • Experience with desktop automation or programmatic control of applications (COM or similar).
  • Domain experience in mechanical engineering, CAD/CAE, PLM, or adjacent industries.
  • Understanding of enterprise deployment constraints on locked-down corporate workstations.
  • Track record contributing to public benchmarks, publications, or open-source agentic AI projects.

Logistics: San Francisco, on-site.

No sponsorship. Relocation not available.



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