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