About The Role

At CAW Studios, we are building the future with agentic AI systems, RAG pipelines, and intelligent automation. From

autonomous AI agents at KnackLabs to developer productivity tools at CodeKnack, we ship production-ready AI

products that solve real problems for enterprises and startups alike.

This is your chance to work on cutting-edge GenAI, LLM fine-tuning, and agent frameworks—and see your code

power products used in the real world. If you're excited about experimenting, shipping fast, and solving complex AI

challenges hands-on, you'll love it here.

Who should apply

Engineer with 2 to 4 years of full-time experience building high-scale software systems, with a proven track record

of deploying complex Generative AI products to production.

Role Overview

We are hiring an AI/ML Engineer II (SE2) to own the architectural implementation and deployment of production-grade

agentic AI systems. This role requires a hybrid of traditional engineering rigour (OOPS, SOLID, high-concurrency)

and advanced AI specialization to build the next generation of intelligent tools.

Responsibilities

Independently design modular and maintainable multi-agent AI systems aligned with SOLID principles
Build high-concurrency, async FastAPI backends for complex AI workloads with enterprise stability
Architect sophisticated agentic workflows using LangGraph with a focus on state persistence and error-recovery
Design and optimize RAG pipelines involving advanced chunking, hybrid search, and re-ranking
Take ownership of containerization and cloud deployment for observable, cost-efficient AI services
Collaborate on reusable AI components and internal frameworks to enhance team engineering velocity

Expectations

Deep obsession with automation, DevOps, OOPS, and SOLID principles
Advanced experience deploying RAG or agent-based systems with LangGraph orchestration
Expert-level mastery of async Python, system thinking, and building scalable backends
High ownership and a production-first mindset for end-to-end system reliability
Hands-on experience across multiple AI modalities (Vision, Audio, Text) and their architectures

Skills:- LangChain, Python, CrewAI, Retrieval Augmented Generation (RAG), FastAPI, Large Language Models (LLM), Docker, Amazon Web Services (AWS), Model Context Protocol (MCP), Vector database, Prompt engineering and LangGraph

AI/ML - Engineer - SE 2