AI Engineer

Your tasks

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

Nice-to-Have

• Java
• Kubernetes
• Dev Containers
• Git Ops
• 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

Your profile

Educational qualification:

BE/B.Tech or Equivalent Degree

Experience :

4-8 Years

Mandatory/requires Skills :

Strong hands-on expertise in Python (or Java), NLP, Reg Ex, Spa Cy, NLTK, and transformer-based models.

Preferred Skills :