Bangalore Urban

Salary
Posted
Jul 27, 2026
Location
Bangalore Urban
Last confirmed open
Jul 28, 2026

What this job asks for AI summary

This role focuses on building production-grade data pipelines and AI-powered applications, sitting at the intersection of data engineering and applied AI. The engineer will design ETL/ELT pipelines on cloud platforms, implement Retrieval-Augmented Generation solutions, and expose AI and data services through REST APIs. It suits someone with solid data engineering experience who has also worked hands-on with LLM frameworks.

Mid level · 3+ years

Must have (8)
DatabricksPySparkPythonLangChain or LlamaindexLLMsRAGFastAPI or FlaskAWS, Azure or GCP
Nice to have (2)
CI/CDMLOps

“or” means any one of them counts — you don't need all of them.

We read this from the posting text with AI. Skim the description below before ruling yourself out.

How this req sits in the market our data

What gives you an edge
LangChain6%RAG8%

Rare in this occupation — lead with these, and say what you built with them.

What won't set you apart
Python55%Databricks42%

Most people in this occupation already list these. Still required — just not what gets you shortlisted.

What the occupation pays Median $142,568 (middle half $111,775–$173,013).

Estimated from BLS employment for this occupation and area, per-skill prevalence across our listing corpus, and published wage benchmarks — as of Jul 28, 2026. It is a model, not a headcount.

Why we read it this way (4)

The role is genuinely split between data pipeline engineering (ETL/ELT, Databricks, PySpark) and AI/LLM application development (LangChain, RAG, FastAPI), making SOC classification ambiguous — 15-1243 (Database Architects) was chosen for the pipeline/data-architecture emphasis, but 15-1252 (Software Developers) is a strong alternative given the application and API development scope.

LlamaIndex is listed alongside LangChain as an interchangeable LLM framework option and is captured in the alternatives field rather than as a separate skill.

DevOps/MLOps practices (CI/CD, model deployment, monitoring) are framed with 'Understanding of' — softer language than the rest of the required section — so CI/CD and MLOps are marked preferred.

Prompt engineering appears in both responsibilities and requirements but is framed as 'familiarity with' in the qualifications block, so it is not captured as a hard gate.

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