Python Developer

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.