Actively Hiring

Senior Gen AI Engineer- RAG & Full Stack - Chennai

Location

Chennai, Tamil Nadu / Remote (Global), Global

Work Mode

Remote, Hybrid

Experience

6-10 Yrs

Salary Range

INR 20-25 LPA

Job Type

Permanent

Openings

1 position

Job Description

Hiring for: A US-based, AI-first data solutions company founded by seasoned technology leaders.

Role: Senior Gen AI Engineer- RAG & Full Stack - Chennai

Positions: 1

Experience: 6 to 10 years

Location(s): Chennai (Hybrid)

Type: Hybrid, Remote / Permanent

Salary: Up to INR 25 LPA (Best as per the fitment)

Notice Period: Immediate to 15 days

Key Responsibilities:

• RAG pipeline end to end: ingestion integration, hybrid retrieval with reranking, prompt/context strategy, citation resolution, refusal behavior

• Permission-aware retrieval: source ACL mapping (SharePoint/Entra, Confluence) to fail-closed retrieval filters; zero-leakage test suite partnership with QA

• Vector database design and operations (Milvus or pgvector): schema, metadata filters,sync, performance

• Full-stack product build: React/TypeScript chat and citation experience, Python/FastAP services, REST APIs, SSO/OIDC integration, admin configuration UI

• Evaluation-driven development: retrieval precision, faithfulness, and citation-accuracy metrics as the daily working loop; A/B testing of retrieval and prompt variants

• Latency engineering to the 3–5s first-token / ~15s complete-answer targets at concurrency

Technical Skills:

• 5+ years software engineering with 2+ years building RAG/LLM applications in production with real users and real quality metrics, not notebooks

• Deep retrieval craft: chunking strategy, embeddings, hybrid search, rerankers; you canexplain why retrieval fails and how you measured the fix

• Genuine full-stack evidence: shipped React/TypeScript front ends AND Python back-end services in production; API design; OIDC/SAML integration

• Vector database production experience (Milvus, pgvector, Weaviate, or equivalent) including permission/metadata filtering

• Evaluation fluency: has built or operated a retrieval/answer quality harness with numeric thresholds

Strongly Preferred:

• Permission-aware/multi-tenant retrieval specifically; Microsoft Graph API; NIM/OpenAI-compatible serving endpoints; streaming UX; enterprise design systems; banking content domains

Screening Questions

Please note that you will be asked to answer these questions during the application process:

1.How many years of experience do you have in software engineering?
2.Current location
3.Current CTC (Lakhs per annum)
4.Expected CTC (Lakhs per annum)
5.Notice period
6.Are you currently serving notice period?
7.How many years of hands-on experience do you have building and deploying RAG/LLM applications in production?
8.Have you worked on a RAG application used by real users in production?
9.Have you worked with Vector Databases in production? Please mention the database(s).
10.Which of the following have you worked hands-on with in production? (Chunking strategies ,Embeddings ,Hybrid search ,Reranking ,Retrieval evaluation/metrics)
11.Do you have production experience building both React/TypeScript frontend and Python backend services?
12.Which Python backend framework have you used in production?
13.Have you implemented permission/metadata-based filtering in a RAG or vector search system?

Skills & Technologies

Required Skills

ConfluenceEmbeddingsFastAPIFull Stack DevelopmentGenAILarge Language Models (LLM)Microsoft Entra IDMicrosoft Graph APIPythonRAG (Retrieval-Augmented Generation)RAG EvaluationReactRerankingSharePointStreamingTypeScriptVector DB

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