About the role
Requirements:
- Strong experience building AI products or AI agents in production environments.
- Experience designing systems where AI agents can take critical or autonomous actions with minimal human intervention.
- Experience building conversational AI systems where users directly interact with the AI.
- Strong understanding of LLM architectures, agent frameworks, orchestration, and reasoning workflows.
- Hands-on experience with frameworks/tools such as LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel, OpenAI APIs, Anthropic APIs, or similar ecosystems.
- Strong backend engineering fundamentals and system design experience.
- Experience working with vector databases, RAG pipelines, memory systems, and evaluation frameworks.
- Proficiency in Python and modern AI/ML infrastructure.
Preferred Qualifications:
- Experience building multi-agent systems or autonomous workflows.
- Experience deploying AI systems at scale in production.
- Familiarity with reinforcement learning, fine-tuning, model evaluation, or AI safety.
- Startup experience or prior founding/early-stage engineering experience.
- Strong product intuition and ability to move quickly in ambiguous environments.
High-Agency AI Systems:
- Candidates should have built AI agents that can take meaningful actions autonomously rather than simply retrieving information or generating responses.
- Examples include AI systems that execute workflows, Agents that make decisions or trigger actions, AI copilots integrated into operational systems, and Autonomous task execution pipelines.
- We expect candidates to clearly explain: The level of autonomy their systems had, What actions the agents could take, and The safeguards, reliability mechanisms, and evaluation methods used.
Conversational AI Experience:
- We are interested in candidates who have built conversational AI products where end users directly interact with AI agents.
- Examples include AI assistants, AI copilots, Chat-based workflow systems, Voice or multimodal conversational interfaces.
- The ideal candidate should demonstrate experience designing user-facing AI interactions rather than only background automation systems.
Technical Stack:
- Stack: Python, FastAPI, Node.js, LangChain, LangGraph, AutoGen, CrewAI, OpenAI, Anthropic, Gemini, open-source LLMs.
- Vector DBs: Pinecone, Weaviate, Chroma, Qdrant, Kubernetes, Docker, AWS/GCP/Azure.
- Observability and evaluation tools for AI systems.