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 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 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
