Applied AI Engineer

Teichert•Sacramento, CA

1d•Onsite

About The Position

This position is responsible for designing, building, and deploying AI agents and applications that solve real business problems across Teichert’s operations. The Applied AI Engineer evaluates large language models, agent design patterns, and traditional machine learning approaches, choosing the right tool for each problem and leads governance of AI agents and skills. This role sits within the Data & Technology Solutions department and contributes to the build-out of Teichert’s AI capabilities and data platform.

Requirements

Bachelor’s degree in Computer Science, Data Science, Software Engineering, or a related field
Minimum 5 years of experience in software engineering, AI/ML engineering, or a similar role, with demonstrated experience building and deploying agentic AI and production applications on top of large language models or other models.
Successful completion of pre-employment drug, alcohol, and background investigation.
Ability to build trust and rapport quickly with employees at all levels, demystifying AI for non-technical audiences, and championing responsible adoption through day-to-day partnership.
Deep knowledge of agent design patterns: reasoning chains, tool orchestration, retrieval-augmented generation, and error handling, independent of vendor.
Ability to evaluate and compare LLMs and foundation models across use cases, making pragmatic tradeoffs on accuracy, cost, latency, and vendor sustainability.
Judgment to know when a classifier, statistical model, or rule is a better fit than an agent.
Strong software engineering skills in Python, including version control, testing, and monitoring; able to build production-grade code, not just notebooks.
Understanding of statistics and decision costs sufficient to design agent evaluation frameworks and confidence thresholds.
Ability to assess data quality for agent inputs and design dashboards to monitor agent performance in production.
Strong communication skills with the ability to translate technical tradeoffs into plain language.
Ability to preserve confidential and proprietary information and successfully avoid conflicts of interest.
Experience working effectively in cross-functional teams and establishing positive working relationships across functions and levels.
Must be able to prioritize, manage multiple concurrent workstreams, and recognize when to seek direction.

Nice To Haves

Master’s degree in related field preferred.
Experience in asset-intensive industries such as construction, mining, energy, transportation, or manufacturing is preferred.
Experience with MLOps/AIOps practices, version control tools (e.g., GitHub, Azure DevOps), Agile software development best practices, AI-assisted coding tools (e.g., Claude Code) to accelerate development; and data visualization platforms (e.g., Power BI, Tableau) preferred.

Responsibilities

Designs and builds AI agents and applications for construction and materials workflows, including document analysis of contracts, specs, and safety plans, schedule risk forecasting, equipment optimization, and bid intelligence, choosing agents, classifiers, statistical models, or simple rules based on what the problem requires.
Evaluates LLMs and foundation models against specific use cases, weighing accuracy, cost, latency, and vendor sustainability.
Architects reasoning chains, tool use, retrieval-augmented generation, and error handling; builds evaluation frameworks and monitors agent performance in production.
Leads governance of AI agents and skills, including risk review and lifecycle management, in partnership with DAAI leadership.
Partners with data engineering to build AI capabilities into Teichert’s data platform.
Serves as a visible ambassador for AI across the organization.
Builds rapport quickly with business and technical stakeholders across Construction Services and Materials; translates business problems into AI designs and communicates tradeoffs and limitations clearly to non-technical audiences.
Transitions from single-agent prototypes to multi-agent orchestration as the platform matures; mentors more junior engineers on agent design and evaluation practices.