Agentic AI expert for Model-Based Engineering

Agentic AI Expert for Model-Based Engineering

Job Summary

We are seeking an Agentic AI Expert to join a research and engineering team developing next-generation Model-Based Design and Model-Based Engineering tools.

In this role, you will help enhance existing engineering software through the integration of modern agentic AI technologies. Your work will focus on improving simulation workflows, code generation, test generation, and diagram generation using large language models, AI agents, retrieval systems, and multi-agent architectures.

You will work as part of a collaborative, research-led team of engineers and technical specialists focused on developing innovative tools for complex engineering applications.

Position Details

Location: Pisa, Italy
Contract type: Full-time leased-worker contract
Working arrangement: On-site

Key Responsibilities

• Support the evolution of proprietary Model-Based Design tools through AI-assisted capabilities.

• Design and integrate agentic AI workflows into existing engineering products.

• Improve large-scale simulation performance and automation.

• Develop AI-assisted code, test, model, and diagram generation capabilities.

• Build and optimise multi-agent systems using retrieval-augmented generation, orchestration, tool calling, and structured workflows.

• Integrate large language model APIs, Model Context Protocol servers, and external tools into engineering applications.

• Analyse and optimise distributed LLM pipelines for latency, scalability, reliability, and token efficiency.

• Develop prompt strategies and agentic patterns for complex engineering tasks.

• Support regression testing and validation of AI-assisted engineering workflows.

• Collaborate with software engineers, systems engineers, researchers, and product stakeholders.

• Contribute to the technical direction and productisation of AI-enabled engineering tools.

Essential Requirements

• Master’s degree in Engineering, Computer Science, or a related technical discipline. A PhD is advantageous.

• Strong programming skills in both Java and Python.

• Experience with AI and machine learning frameworks such as LangChain, LangGraph, Docling, PyTorch, or comparable technologies.

• Hands-on experience with AI coding agents and AI-assisted development tools.

• Experience using large language model APIs, tool calling, structured outputs, and Model Context Protocol servers.

• Strong understanding of agentic workflows, prompt engineering, task decomposition, planning, and reasoning patterns.

• Experience designing and implementing efficient multi-agent AI architectures.

• Practical experience with retrieval-augmented generation, orchestration, memory, routing, and tool integration.

• Experience integrating AI systems into existing software products.

• Ability to analyse and optimise complex distributed LLM pipelines for latency, scalability, throughput, and token usage.

• Familiarity with Model-Based Engineering concepts and tools, including UML, block diagrams, state machines, or simulation models.

• Experience with software testing, particularly regression testing.

• Strong communication, collaboration, and problem-solving skills.

• Self-motivated, product-focused, and able to take ownership of technical work.

• Fluent English and the ability to work effectively in an international environment.

• Willingness to travel internationally when required.

Preferred Qualifications

• Experience with model-to-text, text-to-model, or model-to-model transformations.

• Knowledge of model transformation languages, domain-specific languages, or code generation frameworks.

• Experience with reinforcement learning, model fine-tuning, quantisation, or knowledge distillation.

• Experience developing embedded, real-time, control, automotive, or vehicle software.

• Strong C++ programming skills.

• Experience with the Eclipse Modeling Framework or similar modelling ecosystems.

• Experience with MLOps and private large language model deployments.

• Familiarity with inference and serving platforms such as vLLM.

• Knowledge of CI/CD pipelines, automated testing frameworks, and software delivery practices.

• Experience deploying AI systems in secure, private, or enterprise environments.

Additional Skills

• Strong understanding of software architecture and systems integration.

• Familiarity with distributed AI systems and production LLM applications.

• Ability to translate research concepts into maintainable product features.

• Experience evaluating agent reliability, tool-use accuracy, and workflow performance.

• Understanding of structured model representations, engineering diagrams, and simulation environments.

• Ability to work across research, product, engineering, and testing teams.