Software Engineer

Trimble•Westminster, CO

11d•Onsite

About The Position

Drive Innovation and Build Enterprise AI Solutions as our Next AI Engineer! Architect, deploy, and scale high-impact enterprise AI applications that power connected ecosystems across the entire asset lifecycle. In this hands-on engineering role, you will bridge cutting-edge generative AI with core business workflows, turning complex data and processes into reliable, intelligent production systems. AECO: Trimble is empowering stakeholders through planning, design, construction and the entire asset lifecycle. The company’s innovative and connected ecosystem of solutions improves coordination and collaboration between teams, phases and processes. Automating work and transforming workflows, Trimble is enabling teams to deliver with confidence at every turn. For more information, visit: construction.trimble.com. What Makes This Role Great: In this role, you will be at the forefront of building end-to-end AI capabilities from agent orchestration and RAG pipelines to MCP tool integrations directly shaping how global business workflows operate and setting the standard for production observability and safety.

Requirements

7+ years of software engineering experience with a deep focus on production backend or platform development.
Strong proficiency in Python, including asynchronous programming, robust API development (FastAPI, Flask, Django), and structured data modeling.
Hands-on experience architecting LLM applications, RAG pipelines, conversational systems, and tool-calling agents.
Expertise with API integration patterns, including REST, OAuth 2.0, webhooks, and enterprise SaaS systems.
Strong track record using modern CI/CD tools and containers (Docker, GitHub Actions, Azure DevOps) to manage cloud deployments.

Nice To Haves

Experience with AI orchestration frameworks such as MCP, FastMCP, LangGraph, or CrewAI.
Background with vector search engines, knowledge graphs, or enterprise systems like Salesforce and Contentful.
Practical experience building LLM evaluation frameworks (Ragas, DeepEval, promptfoo, LangSmith) or implementing AI security guardrails.

Responsibilities

Design and launch intelligent conversational agents, RAG platforms, and automated workflows across customer and enterprise operations.
Architect high-performance backend microservices and Model Context Protocol (MCP) tool integrations with enterprise SaaS and data platforms.
Engineer deterministic tool workflows, agent collaboration models, and customer-safe response behaviors for high-stakes business functions.
Implement comprehensive Evals-as-Code, regression testing, and production observability to maintain model accuracy, system reliability, and low latency.
Partner with cross-functional product, platform, and security teams to uncover high-impact automation opportunities and turn them into scalable products.