Senior AI Engineer - Data Modeling

About the Role :

Were not building another chatbot. Were building agentic AI systems that think, decide, and act autonomously inside real enterprises.

Our AI agents dont just respond they navigate data, coordinate with tools, integrate with enterprise systems, and continuously adapt. If youre excited about pushing the boundaries of LLMs, RAG, and multi-agent orchestration into production-grade systems, this role is for you.

Bonus : Using AI tools to enhance your own productivity is expected here not optional.

What Youll Bring :

1. Bachelors degree in Computer Science or equivalent practical experience

2. 5+ years of hands-on experience in Applied ML, NLP, or AI system development

3. Deep familiarity with LLMs (GPT-4, Claude, etc.) - prompt engineering, fine-tuning, API-based deployment

4. Strong experience building RAG pipelines using embeddings and vector databases

5. Understanding of multi-agent systems and task orchestration architectures

6. Proficiency in Python and modern ML frameworks (PyTorch / TensorFlow)

7. Experience with orchestration frameworks such as LangChain, LangGraph, Temporal, n8n, or custom stacks

8. Solid grasp of enterprise engineering principles : version control, CI/CD, API design, monitoring

9. Strong communication skills and ability to thrive in a fast-paced startup environment

What Youll Do :

1. Design and deploy cutting-edge AI agents using our orchestration layer built on n8n, Temporal, and next-gen agentic stacks

2. Build intelligent, context-aware workflows that integrate directly into enterprise systems

3. Prototype quickly and ship production-grade AI agent flows

4. Push the limits of RAG, embeddings, and vector search for real-time grounded intelligence

5. Engineer smart integrations with OpenAI, Anthropic, Claude, and enterprise APIs

6. Lead development of multi-agent orchestration models (planner-executor flows, memory layers, persona-based agents)

7. Own the AI pipeline end-to-end - prompt engineering to deployment and monitoring

8. Set the bar for reliability, latency, and AI safety in production environments