Position: Founding AI Engineer / CTO
About the job Founding AI Engineer / CTO We don't 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.
You'll 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.
You've 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/Type Script/Next.js), 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 you'll 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
(We'll trust you to pick the best tools and make trade-offs, but familiarity with these is ideal)
LLM app frameworks: Lang Chain / 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 + Type Script + Next.js, Tailwind (or similar), Node or Python APIs, Postgre
SQL, Redis, Graph
QL/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 kground 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 (Linked In / Git Hub / 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
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