Responsibilities Cross-Disciplinary Automation
: Design and implement AI workflows that bridge the gap between Systems Engineering and downstream disciplines (HW, SW, Mech) to ensure seamless requirements traceability and consistency.
Physics-Informed Modeling: Develop and deploy Physics-Informed Machine Learning (PIML) models to accelerate simulations in Mechanics and Electrotechnique.
AI-Enhanced System Engineering: Integrate AI-assisted coding, automated unit testing, and bug prediction tools into the software development pipeline in compliance with ASPICE standards.
Predictive Project Management: Build predictive analytics dashboards for Technical Project Managers to forecast resource bottlenecks, budget risks, and milestone delays using historical project data.
Standards & Compliance: Ensure all AI-automated processes and generated outputs adhere to ISO 26262 (Functional Safety) and ISO/SAE 21434 (Cybersecurity) requirements.
Toolchain Integration: Lead the integration of AI agents with existing toolchains, including PLM, ALM (Codebeamer), and MATLAB/Simulink.
Key competencies
Technical Skills
AI and Machine Learning
- Generative AI & LLMs
- Predictive Modeling
- Computer Vision
Data Engineering and Management
- Data Pipeline Construction: Automating the flow of data into AI models.
- Data Quality Assurance: Cleaning and labeling datasets to ensure the AI isn't learning from noise.
Process Automation & Integration (MLOps)
- Workflow Orchestration: Using tools to connect AI outputs to other R&D software (like ELNs or LIMS).
- Deployment: Ensuring the AI tools are accessible via user-friendly interfaces.
Programming & Scripting
- Python
- Javascript / Google Apps Script
- Google Docs / Sheets functions & automation
- HTTP rest API's
Tooling & ALM: ( Plus Knowledge)
- Codebeamer (Requirement Management Tool)
- Google Cloud Platform
Automation & Integration concepts
- AI & Data Handling:
- Basic to intermediate experience with AI tools, APIs, or AI agents
- Interest in applying AI to engineering processes
Soft Skills
- Strong communication and collaboration skills, including ability to translate technical AI capabilities into tangible benefits for the teams
- Ability to work autonomously and proactively
- Analytical mindset with strong problem-solving skills
- User-oriented mindset (training, support, feedback handling)
- Time Managment
Nice to Have
Experience with requirement traceability or compliance activities
Exposure to AI-assisted automation or low-code/no-code platforms
Physics-Informed Machine Learning (PIML)
Predictive Project Analytics
Understanding of system engineering lifecycle and deliverables
Aware About Automotive Industry And Embedded Systems.
Years of Experience 2 to 3 years experience in AI & tools development
