Lead AI Engineer

Job description

You might be our missing piece if you have: Deep expertise in Python and AI frameworks such as PyTorch (ideal), TensorFlow, Keras, or SciPy. Production-grade experience with FastAPI, Flask, or Django for serving AI models and building API-driven applications. Proven experience in architecting and scaling ML systems in production—everything from data pipelines and model deployment to monitoring and optimization. Experience with AI/ML architectures and AI-powered product architectures including: multi-agent architectures, multi-headed models, inference architectures. Experience with connecting data pipelines and serving inference processes. Strong hands-on knowledge of Docker and an understanding of containerized ML workflows (DevOps builds it, but you speak its language). Proficiency in SQLAlchemy, Alembic, and relational databases, along with a solid understanding of how to model, move, and optimize data for ML use cases. Expertise in data wrangling and preprocessing using tools like Pandas, NumPy, or SciPy. Experience integrating with and building on external AI APIs (e.g., OpenAI, Anthropic, Hugging Face, etc.). A solid understanding of software engineering principles, versioning...