AI Engineer

Flipkart · Bengaluru

  • Experience5–9 yrs
  • SalaryNot disclosed
  • Work modeonsite
  • Levelsenior
  • Posted2 Sept 2026
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About Flipkart

Flipkart is hiring in Bengaluru in ecommerce retail. This role looks for around 5+ years of experience.

Skills

  • Retrieval Augmented Generation
  • agentic workflows
  • tool-use patterns
  • LLMs
  • system design
  • ML serving infrastructure
  • database design
  • API development
  • UI integration
  • deployment
  • model distillation
  • quantization
  • inference optimization
  • vLLM
  • unit testing
  • integration testing

The role

A generative AI engineer at a large e-commerce marketplace builds production applications with Retrieval Augmented Generation, agentic workflows, and model optimization, while owning database design, API development, UI integration, and deployment. The role applies LLMs, ML serving infrastructure, and end-to-end software delivery to automate business processes.

Full job description

Responsibilities:

Design end-to-end architecture for mid-sized AI applications.

Work on Model Optimization: Assist DS in basic fine-tuning jobs (LoRA/QLORA) and optimize inference latency.

Evaluate and select the right model (commercial vs. open source) for cost/performance trade-offs.

Mentor Grade 8/9 engineers on AI-native workflows.

Proven ability to ship ML/AI-powered apps.

Understanding of system design and ML serving infrastructure.

Core Responsibilities (All Levels):

Applied GenAI Development: Build production-grade applications using LLMs (GPT-4 Gemini, Llama 3 FK-GPTs).

Implement RAG (Retrieval Augmented Generation), agentic workflows, and tool-use patterns.

End-to-End Engineering: Act as the primary engineering owner for non-tech stakeholders (e. g., automating invoice processing for finance).

You will handle the full SDLC: database design, API development, UI integration, and deployment.

Model Optimization: Collaborate with data scientists to make AI feasible at scale.

Work on model distillation, quantization, and inference optimization (e. g., vLLM, etc. ). AI-First SDLC: Actively use AI tools to automate code reviews, documentation, unit testing, and integration testing.