Role Overview

We are looking for an experienced GenAI/Python Developer to design, build, and deploy production-grade Generative AI applications and intelligent agent-based solutions. The ideal candidate will have strong expertise in Python, LangGraph, LangChain, LlamaIndex, RAG, vector databases, LLM integration, and multi-agent architectures.

The role requires hands-on experience building scalable AI solutions, integrating commercial and open-source LLMs, fine-tuning models, and optimizing GenAI applications for performance, latency, cost, reliability, and production scalability.

Key Responsibilities

• Design, develop, and orchestrate sophisticated Generative AI workflows using LangGraph, LangChain, and multi-agent architectures.
• Build and maintain advanced Retrieval-Augmented Generation (RAG) solutions using LlamaIndex and vector databases.
• Design retrieval pipelines focused on improving the accuracy and relevance of LLM-generated responses.
• Integrate and evaluate multiple commercial and open-source LLMs, including OpenAI, Anthropic, Llama, and Mistral, based on application, performance, and cost requirements.
• Develop scalable and high-performance backend services and APIs using Python, FastAPI, and asynchronous programming.
• Fine-tune and customize LLMs for domain-specific use cases using PyTorch, TensorFlow, Hugging Face, LoRA, and QLoRA.
• Apply advanced prompt engineering techniques to improve LLM response quality and application performance.
• Optimize GenAI workflows for latency, cost, scalability, and reliability.
• Implement monitoring and observability for LLM applications using tools such as LangSmith and Langfuse.
• Containerize AI applications and services using Docker and support production deployments through CI/CD pipelines.
• Deploy and maintain GenAI solutions across cloud environments such as AWS, Azure, or GCP.
• Collaborate with engineering, AI/ML, architecture, and product teams to convert business requirements into scalable AI solutions.
• Troubleshoot and optimize production AI applications, APIs, retrieval pipelines, and model integrations.