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 (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.