Experience: 4 to 8 years

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

- Robust 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.
- Understanding of AI evaluation, grounding, hallucination, confidence and human-in-the-loop workflows.
- Exposure to Systems Engineering, Requirements Engineering or engineering-domain AI is highly desirable.

Rail / Engineering Knowledge

- Exposure to Rail, Automotive, Aerospace, Industrial or other safety-critical engineering domains preferred.
- Understanding of requirements, functional architecture, traceability and V&V; is an advantage.

Education: Bachelor's/Master's degree in Computer Science, AI/ML, Electronics, Electrical, Engineering or related discipline.

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