Founding AI Engineer CTO

We dont want a VP. We seek a true technical co-founder the 0-1 architect who will build the AI core and own it end-to-end.

Youll trade corporate predictability for foundational upside: substantial founder equity a founder stipend and 100% technical ownership. You will be the CTO hands-on shipping product hiring next engineers and setting the engineering culture.

Who you are

• 7 years of hands-on software engineering at the intersection of full-stack and machine learning.
• Youve built and shipped production AI/ML systems (LLM-based products RAG/agent systems embeddings vector search) and you write production code every week.
• You understand the full stack: frontend (React/TypeScript/) backend (Python FastAPI) infra (Docker Kubernetes) databases (Postgres vector DB) and MLOps.
• You care about correctness observability and privacy (audit logs monitoring data governance).

What youll own & ship

• Design and build the core alignment engine: embeddings retrieval match-signal pipeline and ranking and production inference for scale.
• Implement robust retrieval/RAG or agent architectures and make the trade-offs between latency cost and privacy.
• Build data pipelines model evaluation and continuous training workflows and reliable model deployment (serving autoscaling monitoring).
• Lead infra: containerized services cloud infra as code (Terraform) CI/CD and secure model hosting.
• Hire and grow a small engineering team; own product/technical roadmap and KPIs.

Tech stack & skills we expect

(Well trust you to pick the best tools and make trade-offs but familiarity with these is ideal)

• LLM app frameworks: LangChain / agent frameworks for chain-of-responsibility & tool use.
• Vector search & embeddings: experience with Pinecone / Weaviate / pgvector / Redis / Milvus (production tradeoffs for latency cost and scale).
• Fine-tuning & model ops: PEFT / LoRA / QLoRA workflows and Hugging Face toolchain for adapting open models when needed.
• LLM providers & hybrid hosting: pragmatic use of managed LLM APIs (OpenAI Anthropic etc.) plus ability to run/host open models when cost or privacy demands it.
• MLOps & observability: experiment tracking model registry and CI (Weights & Biases MLflow Dagster-style orchestration).
• Full-stack fundamentals: React TypeScript Tailwind (or similar) Node or Python APIs PostgreSQL Redis GraphQL/REST Docker & Kubernetes Terraform.

Nice-to-haves

• Experience with agent-style architectures and knowledge of RAG vs agent trade-offs (security data locality latency).
• Deployment experience on major clouds (AWS/GCP/Azure) and experience optimizing for cost/perf at scale.
• Background in privacy/security GDPR/Audit or working with sensitive data.

The trade

• You bring deep hands-on engineering ML experience and product intuition. You will be the founding technical leader and do the heavy lifting.
• We give you founder equity (no employee option-pool games) a founder stipend and practical ownership of the technical roadmap and hiring.

If this sounds like you

Share your resume and a link to your profile (LinkedIn / GitHub / personal site) and one sentence: what was the hardest technical trade-off you made in the last 12 months (keep it short well take it from there) at