AI Engineer

Experience Summary

• 48 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

Qualifications :

Educational qualification:

BE/ or Equivalent Degree

Experience :

4-8 Years

Mandatory/requires Skills :
Strong hands-on expertise in Python (or Java) NLP RegEx SpaCy NLTK and transformer-based models.

Preferred Skills :

Remote Work :

No

Employment Type :

Full-time