Description :

- Experience : 6-10+ years of software engineering experience, with 1+ years specialized in LangChain and agentic AI.

- Expert Python : Mastery of asynchronous programming (asyncio), backend frameworks like FastAPI, and AI libraries.

- Agentic Frameworks : Deep hands-on experience with LangGraph, CrewAI, or AutoGen for multi-step reasoning.

- LLM Fundamentals : Strong understanding of prompt engineering, reasoning loops (Chain-of-Thought), and fine-tuning.

Good To Have :

- Proficiency in managing vector databases such as Pinecone, FAISS, or Weaviate for advanced RAG.

- Experience with cloud-native environments (AWS/Azure/GCP), Docker, and Kubernetes for scalable AI services.

Roles & Responsibilities :

- Design, develop, and maintain robust Python-based backend services and microservices that power Generative AI features and business-critical applications for enterprise clients.

- Build and optimize LangChain-based pipelines including custom agents, tool integrations, and multi-step reasoning chains to solve complex real-world business problems.

- Lead the end-to-end integration of Gen AI models and APIs into production systems, including prompt design, response validation, and output post-processing logic.

- Collaborate with data scientists and ML engineers to productionize AI models, ensuring seamless model serving, versioning, and monitoring using best DevOps practices.

- Develop and maintain React-based frontend components to support AI-powered dashboards and chat interfaces, ensuring a seamless user experience alongside backend capabilities.

- Conduct thorough code reviews, enforce coding standards, and mentor team members with 0 to 4 years of experience to maintain high quality across the engineering team.

- Participate in system design discussions, architecture reviews, and sprint planning to deliver scalable solutions aligned with business objectives and timelines.

- Troubleshoot and resolve performance bottlenecks, latency issues, and integration failures in Python services and LangChain workflows deployed on cloud infrastructure.