Full Stack AI Engineer

Brains Build is a unified AI platform that brings together data ingestion, analytics, AutoML, research, and deployment into a single workspace for data scientists and ML engineers. I am looking for a hands-on Full-Stack Engineer to help me complete the MVP over the next 2 months on a project contract with a total budget of $3,000.

What you will work on:

• Design, develop and connect existing React/Vite frontends with FastAPI backend endpoints.
• Extend and refine backend APIs for data upload, preprocessing, model training, AutoML runs, and visualization.
• Integrate cloud storage (AWS S3) and help stabilize data flows for large tabular datasets.
• Improve reliability, error handling, timeouts, long-running jobs, and logging/observability.
• Prepare the codebase for repeatable deployment using Docker and basic CI/CD.
• Design and maintain database schemas (PostgreSQL) for users, projects, datasets, experiments, and results.
• Implement and harden authentication/authorization, including role-based access, usage limits and secure API.
• Optimize performance across the stack (query tuning, caching with Redis, frontend rendering, and API latency).
• Connect and manage notebook kernels and execution flows for the in-platform notebook editor environment.
• Build reusable frontend components and state management for Dashboards, and AutoML views.
• Write and maintain unit/integration tests and technical documentation for the code you ship.

You should be comfortable with most of the following:

• Backend: Python, FastAPI (API design and integration), databases such as PostgreSQL/SQL, AWS S3 or similar object storage, Redis, working with notebook kernels and execution environments, basic background jobs/queues.
• Frontend: JavaScript (TypeScript is a plus), React with Vite, Node.js ecosystem (npm/yarn, bundlers), and building reusable components and state management.
• Tools / DevOps: Git, Docker, DCA/CI pipelines (GitHub Actions or similar), basic AWS deployment knowledge (App Runner/ECS/Lambda, CloudWatch, IAM basics), environment/config management.
• Data / ML: Pandas/NumPy, scikit-learn or similar ML libraries, understanding of ML pipelines (preprocessing, training, evaluation, serving), and integrating models behind APIs.
• Extras: Streamlit experience for internal tools, familiarity with experiment tracking/monitoring (MLflow/W&B or similar), and good logging/observability practices.

AI / Data / ML expectations:

• Good understanding of data science workflows (EDA, cleaning, feature engineering, training, evaluation).
• Familiarity with ML concepts (classification/regression, common metrics, pipelines, model deployment via APIs).
• Ability to reason about how backend/infra choices affect ML workflows and experiments.
• Experience in agentic RAG and agentic AI: designing retrieval-augmented pipelines and multi-step agents using frameworks such as Agno, LangChain, CrewAI, LangGraph, or similar.
• Comfortable integrating LLMs via APIs (e.g., OpenAI/Anthropic) into backend services, tools, and user-facing features.

What I am looking for:

• Strong full-stack experience shipping real products, not just demos.
• Ability to read an existing complex codebase and extend it without rewriting everything from scratch.
• Independent, fast, and comfortable making pragmatic technical decisions with a founder who is a data scientist/ML/AI engineer.
• Available to focus on this project over the next 2 months to reach a solid MVP.
• Comfortable working with loosely defined requirements and turning them into clear technical tasks/milestones.
• Strong debugging skills across the stack (frontend, backend, infra, and data/ML issues).
• Habit of writing clean, modular, and well-documented code, including basic tests for critical paths.
• Clear and concise communication in async channels (GitHub issues, messages, short Looms/screenshares).
• Ownership mindset, proactive about spotting edge cases, tech debt, and UX issues and proposing solutions.
• Respectful of security, data privacy, and reliability concerns when working with user data and ML workflows.

Contract details

• Type: Project-based contract
• Duration: ~2 months
• Budget: $3,000 total for the project
• Remote, flexible hours, but with regular check-ins and shared milestones.
• Must provide identification (ID), a recent bank statement, and a VAT statement (if applicable) as part of onboarding for the Brains Build project.