AI/ML - Engineer - SE 2

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 youre excited about experimenting, shipping fast, and solving complex AI
challenges hands-on, youll 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

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• 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