Domain: Generative AI / NLP / Engineering AI / Digital Engineering
Role Overview
We are looking for an AI Engineer to develop an AI-assisted Systems Engineering solution that transforms legacy locomotive engineering information into structured SFS, SDS and traceability artifacts, using LLMs, RAG, semantic search, engineering knowledge and controlled AI workflows.
Key Responsibilities
- Develop AI pipelines for extracting and structuring engineering information from PDF, DOCX, XLSX, XML and other legacy sources.
- Develop RAG-based workflows for retrieving relevant engineering evidence.
- Implement requirement extraction, classification and transformation workflows.
- Develop AI-assisted generation of SFS/SDS candidates using structured outputs.
- Implement semantic search, embeddings and vector-based retrieval.
- Develop traceability recommendation and engineering knowledge workflows.
- Integrate engineering rules and deterministic validation into AI workflows.
- Implement human-in-the-loop review and approval mechanisms.
- Develop confidence scoring,
source grounding and hallucination detection/mitigation approaches.
- Build evaluation datasets and benchmark AI outputs against the manual Systems Engineering baseline.
- Develop APIs and automation using Python.
- Support integration with ALM/PLM systems and engineering tools.
Required AI / Technical Skills
- Solid hands-on Python development.
- Practical experience with Generative AI, LLMs and RAG.
- Experience with frameworks such as LangChain, LangGraph, LlamaIndex or equivalent.
- Experience with embeddings, semantic search and vector databases.
- Knowledge of PostgreSQL/pgvector, FAISS or similar.
- Experience with structured LLM outputs and Pydantic.
- Experience with document processing libraries such as PyMuPDF, Docling, python-docx, openpyxl or equivalent.
- Understanding of knowledge graphs; Neo4j exposure preferred.
- Experience developing REST APIs using FastAPI or similar.