Founding AI Engineer

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.

Evaluation Criteria:

We are specifically looking for candidates who can demonstrate the following:

• 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 that are integrated into operational systems, and autonomous task execution pipelines.
• We expect candidates to clearly explain: The level of autonomy their systems had, and what actions the agents could take. 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:

• 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.