Artificial Intelligence Engineer

AI Engineer Profile Summary AI Engineer focused on building and operating

LLM-powered solutions

for

legal and regulatory document workflows

(e.g., EU AI Act, DSA, Data Privacy, ESG, Security, Compliance). Strong emphasis on

reference identification & resolution

,

retrieval quality

,

traceability

, and

evaluation

in document-centric AI systems.

Core Responsibilities / Focus Design and implement

GenAI / Agentic AI

applications for complex document understanding and decision support Build

RAG pipelines

tailored to regulatory/legal texts (high precision, citation grounding, explainability) Develop robust ingestion and retrieval strategies, including

contextual chunking

,

embeddings

, and metadata enrichment Integrate

Knowledge Graphs (RDF/SPARQL)

with LLM workflows for structured + unstructured reasoning Orchestrate multi-step AI workflows using

LangChain / LangGraph

(and similar agent frameworks) Establish

AI quality assurance

and evaluation practices (e.g., retrieval quality, hallucination checks, judge-based evals, RAGAS-style scoring) Build & train / fine-tune

specialized NER-related models Support

model lifecycle

activities (experimentation, versioning, deployment readiness, 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)

Preferred / 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