- 8 - 14 years of experience in software engineering (backend or fullstack)

- 2+ years of hands-on experience with LLMs (RAG, agents, prompt engineering)

- Strong experience building production-grade distributed systems

- Proficiency in Python

- Strong experience with SQL & NoSQL databases

- Hands-on experience with LangChain, LangGraph, or similar frameworks

- Experience deploying systems on AWS / Azure / GCP

- Strong understanding of APIs, microservices, and system design

- Experience with vector databases (Pinecone, Weaviate, FAISS, etc.)

- Familiarity with CI/CD pipelines and DevOps practices

Roles & Responsibilities :

- Develop and optimize LLM-based solutions : Lead the design and deployment of large language models, leveraging techniques like prompt engineering, retrieval-augmented generation (RAG), and agent-based architectures.

- Codebase ownership : Build and maintain/review high-quality, efficient code in Python (using frameworks like LangChain/LangGraph) and SQL, focusing on reusable components, scalability, and performance best practices.

- Cloud integration : Aide in deployment of GenAI applications on cloud platforms (Azure, GCP, or AWS), optimizing resource usage and ensuring robust CI/CD processes.

- Cross-functional collaboration : Work closely with product owners, data scientists, and business SMEs to define project requirements, translate technical details, and deliver impactful AI products.

- Mentoring and guidance : Provide technical leadership and knowledge-sharing to the engineering team, fostering best practices in machine learning and large language model development.

Good to Have :

- Exposure to frontend frameworks (React, Next.js) for fullstack roles

- Understanding of model evaluation, fine-tuning, or LLMOps

- Experience building multi-tenant or enterprise SaaS platforms