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Photon

AI QA Engineer - Bangalore, IND

Reposted 2 Days Ago
In-Office or Remote
Hiring Remotely in India
Mid level
In-Office or Remote
Hiring Remotely in India
Mid level
Develop and deploy production-grade Generative AI services using LLMs, RAG, embeddings and vector retrieval. Build APIs and microservices with FastAPI, integrate models and enterprise workflows, optimize model accuracy and latency, implement monitoring/logging/evaluation frameworks, and support cloud/Docker deployments.
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Job Summary

We are seeking a Generative AI Engineer with strong Python expertise to build enterprise AI applications leveraging LLMs, RAG, embeddings, and intelligent automation. The role focuses on developing production-grade AI services, integrating foundation models, and delivering business solutions powered by Generative AI.

Key Responsibilities
  • Build AI-powered applications using OpenAI, Claude, Gemini, Llama, and Hugging Face models
  • Develop RAG pipelines using LlamaIndex and LangChain
  • Implement prompt engineering, embeddings, semantic search, and vector retrieval
  • Build APIs and microservices using FastAPI and Python
  • Integrate AI solutions with enterprise applications and workflows
  • Evaluate and optimize model accuracy, latency, and reliability
  • Develop monitoring, logging, and evaluation frameworks for AI applications
  • Support deployment and production operations of AI workloads
Required Skills Mandatory
  • 3+ years of Python development experience
  • Experience with OpenAI, Claude, Gemini, or Hugging Face
  • Experience with LangChain or LlamaIndex
  • Experience with Vector Databases and Embeddings
  • Experience building APIs using FastAPI
  • Understanding of RAG architectures
  • Docker and Cloud deployment experience
  • Strong prompt engineering skills
Preferred
  • LangGraph
  • Agentic AI concepts
  • Fine-tuning and PEFT techniques
  • AWS, Azure, or GCP
  • LangSmith / LangWatch
  • MLflow or experiment tracking
Interview Focus Areas

Candidates should be able to explain:

  • AI applications delivered
  • RAG architecture used
  • Embedding and retrieval strategy
  • Prompt optimization techniques
  • Production deployment approach
  • Monitoring and evaluation mechanisms
  • Business challenges solved using GenAI

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