AI Engineer

Location : Bangalore | Experience : 4-7 years

- 47 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).
- Robust 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 Role & responsibilities

📌 Artificial Intelligence Engineer (Bengaluru)
🏢 Armel u0026 Alina Technologies
📍 Bengaluru