About the role
Responsibilities:
- 0-1 Architecture: Own the setup, architecture, and evolution of AI/ML practices, tooling, and infrastructure.
- Agent and Feedback Systems: Design reinforcement learning (RL), feedback loops, and learning systems for continuous model and agent performance improvement.
- Production Deployment: Take models and GenAI/LLM pipelines from research/prototypes directly into scalable production.
- Cross-functional Collaboration: Partner closely with product, design, and backend engineering to embed AI capabilities natively into the IDE.
Requirements:
- Experience: 4 to 12 years of strong software engineering and machine learning/AI experience.
- Tech Stack: Python, deep familiarity with LLMs, RAG, embeddings, vector databases, AI agents, and agent frameworks (LangChain, LangGraph, LlamaIndex).
- Systems Expertise: Full-stack understanding from core model training/evaluation to cloud infrastructure (AWS, GCP, Azure) and production observability.
- Leadership Style: Ability to operate as a hands-on builder and technical mentor rather than a traditional people manager.