Full Stack AI Engineer

Role overview

Build and ship AI-powered products across the full stack, from model and API integration through backend data pipelines, user interfaces, deployment, and production operations. This founding-level engineering role offers broad ownership and requires strong product judgment, comfort with ambiguity, and the ability to turn an evolving idea into a working, observable service.

Responsibilities

- Design, implement, test, and release AI-enabled features and products end to end. - Integrate large language models, machine-learning models, and external AI APIs into production applications. - Build backend services, APIs, and event-driven data pipelines using PostgreSQL, Redis, Kafka, and Flask-based services. - Develop responsive, performant interfaces with TypeScript, React, and Next.js. - Operate the deployment lifecycle, including Docker, CI/CD, AWS infrastructure, monitoring, and incident response. - Work with PostgreSQL vector embeddings, Redis caching and sessions, and Kafka event streams. - Collaborate with founding leadership on product direction, architecture, and technical priorities. - Evaluate new AI frameworks, tools, and APIs as the ecosystem changes.

Requirements

- At least three years of full-stack development experience across frontend and backend systems. - Strong TypeScript and React/Next.js skills, plus proficiency in Python, Node.js, Go, or another backend language. - Hands-on experience integrating AI or machine-learning models and LLM APIs into applications. - Production experience with Docker, AWS services, and CI/CD deployment pipelines. - Direct experience with PostgreSQL, including pgvector, Redis, and Kafka-based architectures. - Understanding of relational data, caching, sessions, and event-driven modeling. - Ability to turn vague problems into shippable solutions and make pragmatic technical decisions. - Strong product sense and concern for the usefulness of what you build.

Nice to have

- Experience with vector databases, retrieval-augmented generation, or multi-agent systems. - Familiarity with MLOps, model serving, and inference optimization. - Experience with Kafka KRaft mode, Redis clusters, Docker Compose, or AWS infrastructure. - Prior startup or founding-engineer experience, open-source contributions, or technical writing.

Benefits and work setup

- Full-time remote role with high ownership and direct involvement in product and architecture decisions.