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 (Git), and Python code style (PEP 8).
A track record of leading AI/ML projects from ideation to deployment.
Experience mentoring and guiding engineers, helping them grow technically and strategically.
We would be thrilled if you have:
Exposure to research or academic papers, and a knack for translating theory into production-ready code.
Comfort with cloud infrastructure (AWS, Azure, GCP, or DigitalOcean) and understanding of CI/CD best practices.
Hands-on experience with LLMs, embeddings, semantic search, and tools like StableDiffusion or LLaMA.
A collaborative mindset and the ability to bridge the gap between product, engineering, and research teams.
We will be working together on the following:
Understanding clients' and their requirements and providing optimal solutions.
Staying ahead of industry trends and implementing cutting-edge solutions.
Technical leadership over major AI initiatives, from architecture design to final delivery.
Mentoring and upskilling the AI team—pair programming, code reviews, and continuous learning are your bread and butter.
Driving best practices around model development, reproducibility, monitoring, and scalability.
Staying up to date with state-of-the-art advancements and continuously leveling up our AI capabilities.
