Engineer I, Artificial Intelligence

The Role:
The core purpose is the mission of the job - why it needs to be filled and how it fits into the organization. By narrowly defining the Core Purpose, you're able to define what's needed to solve the existing business challenge(s)

The mission of this role is to design, develop, deploy, and operationalize agentic AI systems and scientific machine learning solutions that automate complex, multi-step technical workflows.

The AI Engineer will focus on building LLM-driven, goal-oriented AI agents and data-driven models for physical systems, integrating them with data, tools, sensors, and simulation workflows.

This is a hands-on, implementation-focused role suited for someone passionate about

agentic AI, scientific ML, and real-world engineering problem solving. Exposure to Modeling &Simulation (CFD/FEA) is beneficial but not mandatory.

In this role you will:
What are the expected outcomes What must this role get done in order to meet your business objectives Define what success will look like.

• Design and develop agentic AI systems for multi-step reasoning, tool usage, and workflow orchestration
• Build LLM-driven workflows and Python-based pipelines integrating agents with data, APIs, and engineering tools
• Develop and apply scientific machine learning models using experimental, sensor, and simulation data
• Create data pipelines for preprocessing, feature extraction, and integration of time-series and spatial data
• Deploy and operationalize AI/ML models and agents as scalable services or APIs
• Implement monitoring, evaluation, and validation for both agent systems and ML models
• Visualize data and model outputs to support analysis and decision-making
• Collaborate with domain experts to integrate AI into engineering and simulation workflows, and document reusable solutions

Traits we believe make a strong candidate:

What are the Position Specific Competencies Define the skills/competencies necessary to do the job. These should tie directly back to the purpose and outcome.

• Bachelor's degree (minimum) in Mechanical Engineering, Computer Science, or a related engineering discipline
• 1-3 years of relevant work experience in AI, ML, software engineering, or applied research roles (industry, startup, or research labs)
• Strong proficiency in Python programming

• Hands‑on experience building agentic AI systems that includes multi‑step task execution, Tool/function calling and workflow orchestration across agents or components

• Practical experience with machine learning libraries, including NumPy, Pandas, SciPy, scikit‑learn, TensorFlow and/or PyTorch
• Ability to independently design, build, and debug end‑to‑end AI workflows

Candidates with a demonstrable showcase project will be strongly preferred. Examples include (but are not limited to):

• An agentic AI system that automates a complex multi‑step task (engineering, data analysis, design, or simulation related)
• A GitHub, internal demo, or portfolio project demonstrating, agent orchestration, use of tools/APIs, non‑trivial decision logic or reasoning loops
• Integration of LLM agents with data processing, visualization, or external software tools

The project does not need to be simulation‑focused, but relevance to engineering workflows is a plus

Experience with CFD or FEA workflows, particularly involving geometry, meshing, or simulation post‑processing will be considered as an advantage

Familiarity with open‑source engineering tools such as Open FOAM, SU2, CalculiX or similar will be considered as an advantage

Your success will be measured by:
Success for the role is not defined solely on the outcome of a project but rather a combination of the results and how these results were achieved. Describe the PACE value competencies that are required.

. Effectiveness of agentic AI and SciML systems in real workflows

. Quality, scalability, and maintainability of deployed AI systems

. Demonstrated impact in reducing manual effort and improving engineering workflows

. Ability to translate ambiguous physical systems problems into structured AI/ML solutions

. Strong collaboration across AI, simulation, and experimental teams