Staff AI Engineer (LLM)

How this role works

You will start by building the system as part of our team, working in a focused, greenfield setup. After a few months of introduction to the project , you will seamlessly continue working on the same product directly with the client , becoming part of the client’s team responsible for its long-term development and scaling. There is no handover phase and no context switching - you stay with the same codebase, product, and technical challenges, while gaining long-term ownership and impact.

What you’ll work on

• Designing, implementing, and deploying end-to-end NLP and deep learning systems
• Building LLM-powered applications that interact with real users
• Developing and maintaining production Python services
• Exposing models and pipelines via REST APIs (FastAPI, Flask)
• Working on retrieval models and techniques (RAG, embeddings, ranking)
• Evaluating, monitoring, and continuously improving model and system quality
• Scaling systems to handle enormous volumes of requests

Biggest challenges in this role

• Greenfield project built from scratch
• High-scale, user-facing systems with strict performance and reliability requirements
• Designing systems meant for long-term ownership , not short-term delivery
• Balancing model quality, latency, and cost in production LLM systems

What you’ll learn

• How to build LLM-powered products from scratch and take them to production
• Proven approaches to running LLMs in production at scale
• How to design, evaluate, and evolve NLP systems used by real users
• Best practices for production ML and AI system architecture

What you’ll get to try and experiment with

• End-to-end ownership of LLM-based systems
• Optimizing retrieval models, RAG pipelines, and inference workflows
• Experimenting with different LLMs, prompting strategies, and system designs
• Solving performance and reliability challenges under heavy traffic