The Role:

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

The AI Engineer will focus on building LLM-driven, goal-oriented AI agentsand 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 systemsfor multi-step reasoning, tool usage, and workflow orchestration

• Build LLM-driven workflows and Python-based pipelinesintegrating agents with data, APIs, and engineering tools

• Develop and apply scientific machine learning modelsusing experimental, sensor, and simulation data

• Create data pipelinesfor preprocessing, feature extraction, and integration of time-series and spatial data

• Deploy and operationalize AI/ML models and agentsas scalable services or APIs

• Implement monitoring, evaluation, and validationfor 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’sdegree(minimum)inMechanical Engineering, Computer Science, or a related engineering discipline

• 1–3years of relevant work experience inAI, ML, software engineering, or applied research roles(industry, startup, or research labs)

• Strong proficiency in Python programming

• Hands‑on experience building agentic AI systemsthat includesmulti‑step task execution,Tool/function callingand workflow orchestration across agents or components

• Practical experience with machine learning libraries, including NumPy, Pandas, SciPy, scikit‑learn, TensorFlow and/orPyTorch

• Ability to independently design, build, and debug end‑to‑end AI workflows

Candidates with a demonstrable showcase projectwill 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 notneed 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‑processingwill beconsidered as an advantage

• Familiarity with open‑source engineering toolssuch asOpenFOAM, SU2,CalculiXor similarwill be considered as an advantage

Your success will be measured by:

• Effectiveness ofagenticAI andSciMLsystems 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