AI Analyst

Allegis Group; Pune District; On-site; INR 1,800,000 - 3,200,000; Full time

Job summary

Allegis Group is seeking an experienced GenAI/LLM engineer to design and deliver enterprise AI applications. You will build back-end APIs with Python and FastAPI, implement RAG pipelines, and optimize prompts for accuracy and reliability.

You will validate AI outputs against business data, collaborate with data teams, and ensure governance and security best practices across production systems. Strong experience with vector stores and orchestration tools is required.

Qualifications

• Extensive software development experience with focus on GenAI/LLM engineering.
• Hands-on Python backend API development and containerization.
• Experience building production-grade LLM/RAG/Agentic AI apps.
• Experience with orchestration frameworks and vector stores.
• Familiarity with enterprise AI platforms and data models.

Responsibilities

• Design, build, and optimize enterprise Generative AI apps using LLMs and RAG.
• Validate AI outputs against business data for accuracy and alignment.
• Develop prompt strategies to improve quality and reduce hallucinations.
• Build/optimize RAG pipelines with vector databases and semantic search.
• Implement AI evaluation frameworks for accuracy, latency, and quality.
• Collaborate with data engineering and business teams to embed AI in apps.
• Develop backend services and APIs using Python and FastAPI.
• Monitor production AI systems and perform root cause analysis.
• Ensure Responsible AI, security, governance, and enterprise best practices.

Skills

Python

FastAPI

LLM

RAG

Agentic AI

LangChain

LangGraph

Vector databases

Embeddings

Prompt Engineering

Azure OpenAI

OpenAI APIs

AWS Bedrock

SQL

Docker

Git

CI/CD

Tools

Azure AI Search

Pinecone

ChromaDB

FAISS

OpenSearch

Job description

Role & responsibilities

• Design, build, and optimize enterprise Generative AI applications using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG).
• Validate AI-generated responses against business data sources (e.g., Looker Explore/semantic models) to ensure factual accuracy and semantic alignment.
• Develop and optimize prompt engineering strategies to improve response quality, consistency, and reduce hallucinations.
• Build and enhance RAG pipelines using vector databases, embeddings, semantic search, and retrieval optimization techniques.
• Implement AI evaluation frameworks to measure response accuracy, relevance, faithfulness, latency, and overall LLM performance.
• Collaborate with data engineering, analytics, and business teams to integrate AI capabilities into enterprise applications.
• Develop scalable backend services and APIs using Python and FastAPI for AI solutions.
• Monitor production AI systems using observability tools, perform root cause analysis, and continuously improve model performance.
• Ensure AI solutions follow Responsible AI, security, governance, and enterprise best practices.

Preferred candidate profile

• 610+ years of overall software development experience with 2–4+ years in Generative AI/LLM engineering.
• Strong hands‑on expertise in Python and backend API development (FastAPI preferred).
• Experience building production‑grade LLM, RAG, and Agentic AI applications.
• Hands‑on experience with LangChain/LangGraph or similar orchestration frameworks.
• Experience working with Vector Databases such as Azure AI Search, Pinecone, ChromaDB, FAISS, or OpenSearch.
• Strong understanding of Prompt Engineering, embeddings, semantic search, hybrid retrieval, and reranking techniques.
• Experience implementing AI Evaluation/Validation frameworks (RAGAS, DeepEval, LLM-as-a-Judge, hallucination detection, response quality evaluation).
• Familiarity with Azure OpenAI, AWS Bedrock, OpenAI APIs, or similar enterprise AI platforms.
• Strong knowledge of SQL and enterprise data models.
• Experience with Docker, Git, CI/CD, and cloud deployment is preferred.
• Excellent analytical, debugging, stakeholder communication, and problem‑solving skills.
• Experience working in enterprise, banking, financial services, or large‑scale production environments is an added advantage.