AI Engineer

Experience Summary

• 4–8 years of experience as an AI Engineer focused on building and operating LLM-powered solutions for legal, regulatory, and compliance document workflows (e.g., EU AI Act, DSA, Data Privacy, ESG, Security, Compliance).

• Strong emphasis on reference identification, citation grounding, retrieval quality, traceability, explainability, and evaluation in document-centric AI systems.

Core Responsibilities / Focus

• Design and implement GenAI and Agentic AI applications for complex document understanding, reasoning, and decision support

• Build and optimize RAG (Retrieval-Augmented Generation) pipelines tailored to regulatory and legal documents with high precision and grounded responses

• Develop robust document ingestion and retrieval strategies including contextual chunking, embeddings, metadata enrichment, and semantic indexing

• Implement reference identification, citation tracking, and traceability mechanisms for document-centric AI workflows

• Optimize retrieval ranking, semantic search, and grounding to improve answer accuracy and reduce hallucinations

• Integrate Knowledge Graphs (RDF/SPARQL) with LLM workflows for structured and unstructured reasoning

• Orchestrate multi-step AI workflows using LangChain, LangGraph, or similar agent frameworks

• Establish AI quality assurance and evaluation practices including retrieval evaluation, hallucination detection, LLM judge frameworks, and RAGAS-style scoring

• Build, train, and fine-tune specialized NER and document understanding models

• Ensure explainability, auditability, and compliance of AI outputs in regulated environments

• Support end-to-end model lifecycle activities including experimentation, versioning, deployment readiness, and monitoring handover

Core Skills (Must-Have)

• Python (primary)

• Docker / Docker Compose

• NLP / NLU

• GenAI / LLM application development

• Agentic AI

• RAG (Retrieval-Augmented Generation)

• Embeddings

• Contextual chunking strategies

• Knowledge Graphs (RDF)

• SPARQL

• Model lifecycle / ML application lifecycle

• LangChain

• LangGraph

• Git

• AI QA / evaluation (e.g., RAGAS, LLM judges, retrieval and answer quality validation)

Nice-to-Have

• Java

• Kubernetes

• Dev Containers

• GitOps

• Documentation practices

• Deepagents (or similar advanced agent frameworks)

Domain Advantage

• Experience with legal, regulatory, compliance, and policy documents

• Understanding of requirements around auditability, explainability, and risk controls in AI systems