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
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