Job description
Job Title: Engineer I, Artificial Intelligence Job Description: The Role: 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: 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: 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 m ul ti ‑ step task execution , Tool/function calling and w orkflow orchestration across agents or components Practical experience with machine learning libraries , including Num.
Py, Pandas, Sci. Py, scikit ‑ learn, Tensor. Flow and/or Py. Torch 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 , a gent orchestration , u se of tools/APIs , n on ‑ 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 OpenFOAM , SU2, CalculiX or similar will be considered as an advantage Your success will be measured by: 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