GenAI/Applied AI

Key Responsibilities

• Build and maintain RAG pipelines, prompt/agent workflows, and evaluation harnesses for internal engineering use cases
• Support fine-tuning and domain adaptation of LLMs for automotive requirements engineering and compliance documentation
• Work on reducing hallucination and improving reliability via knowledge-graph grounding and auto-evaluation frameworks
• Prototype AI-assisted tools for requirements drafting, traceability, and test-case generation supporting the ASPICE pipeline
• Explore applied-AI use cases beyond engineering (business development intelligence, HR process automation) under the AI+X mandate
• Document and present findings and working prototypes to engineering and product stakeholders
  
  

A Typical Day

• Morning: check evaluation dashboards and overnight pipeline runs; triage failures and hallucination/quality regressions
• Morning: build and iterate on RAG pipelines, prompts, and agent workflows for the ASPICE SOW-to-artifact use case
• Mid-day: pair with a Systems Software Engineer to test generated requirements or test cases against real project data and capture feedback
• Afternoon: dataset preparation, fine-tuning or domain-adaptation experiments, knowledge-graph grounding work
• Afternoon: extend the evaluation harness — add cases, measure accuracy and hallucination rate, log results
• Late day: document findings, update the prototype backlog, prepare short demos for stakeholders
• Weekly rhythm: review with the GenAI/Applied AI Lead on use-case prioritisation; periodic AI+X discovery sessions with SW, HW, BD and HR
  
  

Required Skills

• Strong foundation in Python and NLP/deep learning fundamentals
• Familiarity with LLM APIs (Anthropic, OpenAI, or similar) and RAG concepts
• Understanding of prompt engineering and basic agent-framework concepts
• Analytical mindset and comfort with ambiguity typical of a proving-ground role
  
  

Good-to-Have Skills

• Exposure to automotive or engineering-domain data
• Experience with workflow-automation frameworks (e.g. n8n)
• Familiarity with evaluation/observability practices for LLM systems (hallucination reduction, auto-eval)
  
  

Standards & Compliance Exposure

No domain-standard compliance required at this level; awareness of ASPICE terminology is a plus given the pipeline it supports

Education

Bachelor's/Master's in Computer Science, AI/ML, or related field

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