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AI QA Engineer - Bangalore, IND at Photon

, karnataka, India -

Full Time

Start Date

Immediate

Expiry Date

29 Sep, 26

Salary

0.0

Posted On

01 Jul, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Generative AI, LLMs, RAG, LangChain, LlamaIndex, FastAPI, Vector Databases, Prompt Engineering, Docker, Cloud Deployment, Embeddings, Semantic Search, Microservices, Model Evaluation, Agentic AI

Industry

IT Services and IT Consulting

Description

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

Responsibilities

Develop production-grade enterprise AI applications using LLMs, RAG pipelines, and intelligent automation. Focus on building APIs, optimizing model reliability, and integrating AI solutions into enterprise workflows.