Principal GenAI Engineer – Backend / Fullstack

What We’re 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