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.