Were Looking For:

- 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