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.
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
- 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
- LangGraph
- Agentic AI concepts
- Fine-tuning and PEFT techniques
- AWS, Azure, or GCP
- LangSmith / LangWatch
- MLflow or experiment tracking
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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