Lead Agentic AI Engineer

Imagine redefining the global movement of freight by building sophisticated GenAI agents that bridge the gap between complex data and real-world action. At Trimble, you will have the unique opportunity to lead the technical evolution of our transportation network, crafting autonomous solutions that make global shipping safer, smarter, and more efficient for everyone involved.

In this role, you will be the technical visionary owning the end-to-end delivery of mission‑critical GenAI agent initiatives. You will not just be writing code, you will be defining the component‑level architecture and orchestration patterns that will set the standard for how Generative AI transforms the entire logistics industry.

• Own the end‑to‑end technical direction and architectural patterns for high‑impact GenAI initiatives, defining how we use agent orchestration graphs and RAG pipelines at scale.
• Establish and evolve the CI/CD and automated evaluation pipelines for non‑deterministic AI outputs, taking full ownership of SLIs/SLOs and production integrity for your services.
• Lead the evaluation and prototyping of cutting‑edge techniques (e.g., RLHF variants, DSPy, advanced retrieval) to make high‑stakes, data‑driven decisions for our AI roadmap.
• Implement sophisticated observability hooks and monitoring strategies (e.g., using Lang Smith) to trace agent logic, optimize performance, and lead root‑cause analysis for production incidents.
• Drive the experimentation loops required to hit aggressive accuracy, latency, and cost targets, ensuring our "Agentic" solutions are both cutting‑edge and commercially viable.
• Act as the principal design authority and code reviewer for the team, mentoring P1–P3 engineers and championing "secure‑by‑design" practices.

• You bring 8 to 15 years of robust engineering experience, preferably within a Tier‑1 organization.
• You are a Python expert with a deep understanding of developing and scaling high‑quality code.
• Proven expertise in developing LLM applications and working with RAG frameworks including hybrid search, vector DBs, and ANN algorithms in a professional environment.
• Strong background in defining component‑level architecture and influencing team patterns for agentic workflows.
• Deep understanding of implementing automated evaluation pipelines for non‑deterministic AI outputs, including "LLM‑as‑a‑judge" frameworks and benchmarking for accuracy and safety.
• Experience with LLM observability and analytics tools such as Datadog or Databricks to monitor performance at scale.
• Deep understanding of Agile delivery and the ability to handle production issues across multiple time zones.

• GenAI Passion:
  You have a portfolio of personal projects, a technical blog, or a Git Hub repository demonstrating your hands‑on experimentation with the latest LLM frameworks.
• Community Contribution:
  You’ve contributed to open‑source AI libraries (e.g., Lang Chain, Llama Index, or AutoGPT) or have a history of sharing insights at technical meetups.