AI Engineer

Daimler Truck•Portland, OR

5d•Hybrid

About The Position

The AI Strategy team within the Data Intelligence Hub (DIH) leverages enterprise data to build agentic workflows, copilots, and reusable AI components that transform how the organization designs, manufactures, sells, and supports vehicles. By combining AI engineering, workflow automation, data engineering, and scalable architectural patterns, the team delivers enterprise‑wide capabilities that propel the company toward an intelligent, AI‑accelerated future. In this highly strategic and hands‑on role, the AI Engineer will help drive the company’s shift toward an agent‑first operating model where intelligent systems proactively support employees, automate decisions, and streamline operations across the business. Acting as a forward‑deployed developer in business domains, you will help design, build, and operationalize secure, scalable, governed AI systems in tight partnership with product owners, platform architects, and domain leaders. You will also help shape the shared AI ecosystem that connects these capabilities across the enterprise, ensuring that every solution benefits from and contributes to a common foundation. You help will convert strategic opportunities into shipped, measurable outcomes while championing innovation, reliability, and responsible AI practices.

Requirements

Bachelor’s degree in a technical, analytical, or related field and 2-4 years of experience applying to AI in real organizations, is required
Demonstrated ability to use LLMs, copilots, and agentic tools to accelerate workflow design, analysis, documentation, and prototyping.
Experience using AI tools to design AI‑enhanced workflows that solve real business problems, including RAG, tool orchestration, and structured reasoning.
Understanding how AI systems behave in production, including evaluation, guardrails, reliability, governance, and safety considerations.
Has contributed to proof‑of‑concepts or pilot projects, turning exploratory ideas into validated business outcomes.
Ability to assess AI vendors, platforms, and emerging technologies for scalability, security, feasibility, and strategic fit.
Familiarity with enterprise data and integration concepts (Snowflake, Azure, operational systems) and how AI solutions connect to them.
Understanding responsible AI practices, including bias mitigation, transparency, explainability, and safe model use.
Communication skills—able to explain AI concepts, risks, and decisions to stakeholders without technical backgrounds.
Proven ability to work cross‑functionally with product owners, architects, engineers, and business leaders to deliver measurable impact.
Creative, strategic problem-solver, comfortable managing multiple initiatives and translating ideas into practical solutions.

Responsibilities

Design and orchestrate intelligent agents that automate multi‑step workflows and enhance decision‑making across the enterprise.
Define reusable patterns for agentic frameworks, copilots, and AI services that scale across domains.
Implement structured prompting, guardrails, and chain‑of‑thought controls to reduce hallucinations and improve step‑level accuracy.
Build evaluation suites to measure agent behavior, retrieval accuracy, reasoning quality, and workflow performance.
Implement automated behavioral evals, regression tests, and scenario‑based scoring.
Analyze validation results to guide model refinement and reduce operational risk.
Contribute to improving workflows, evaluation practices, and reusable patterns over time as experience grows
Prepare datasets, run fine‑tuning jobs using provided recipes, and adjust retrieval settings following team guidance.
Translate business opportunities into actionable AI technical designs.
Support prototyping activities by building small components or proofs of concept under direction.
Evaluate vendor technologies for scalability, security, and strategic fit.
Assist in internal innovation initiatives, learning programs, and capability enablement.
Apply responsible for AI standards including transparency, bias mitigation, and robust documentation.
Work within cross‑functional teams to implement assigned AI components and share progress updates with stakeholders.