Director of Engineering

About Fusemachines

Founded in 2013, Fusemachines is a global provider of enterprise AI products and services, on a mission to democratize AI. Leveraging proprietary AI Studio and AI Engines, the company helps drive the clients’ AI Enterprise Transformation, regardless of where they are in their Digital AI journeys. With offices in North America, Asia, and Latin America, Fusemachines provides a suite of enterprise AI offerings and specialty services that allow organizations of any size to implement and scale AI. Fusemachines serves companies in industries such as retail,  manufacturing, and government.
Fusemachines continues to actively pursue the mission of democratizing AI for the masses by providing high-quality AI education in underserved communities and helping organizations achieve their full potential with AI.
Preferred Location: New York (Hybrid)
About the Role:
We are seeking a Director of Engineering with balanced expertise in core software engineering with knowledge of AI/Machine learning to spearhead our engineering initiatives for innovative products. This role demands a leader who not only has a profound technical grounding in both ML/MLOps and software development but also possesses the strategic acumen to merge these disciplines seamlessly. We aim to find a leader who can drive our engineering team towards excellence in both machine learning innovation and robust software engineering practices.

• Strategic Leadership: Develop and execute a cohesive strategy that equally emphasizes advancements in ML/MLOps and core software engineering practices, ensuring they collectively support and enhance our product's vision and capabilities.
• Engineering Excellence: Oversee the development, scaling, and optimization of our ML platform while ensuring the software engineering foundations are solid, scalable, and maintainable. This includes leading efforts in system architecture, API design, data processing, and infrastructure that supports both machine learning and application development.
• ML/MLOps Innovation: Guide the team in adopting and innovating in the areas of machine learning model development, deployment, monitoring, and management. Ensure the ML lifecycle is fully integrated with our CI/CD pipelines, Kubernetes, emphasizing automation, reproducibility, and scalability.
• Software Development Leadership: Champion best practices in software development, including design patterns, code quality, security, and performance. Ensure that our core software engineering practices enable and enhance our ML capabilities, fostering a culture of excellence.
• Team Building and Mentorship: Lead, mentor, and lead a diverse distributed engineering team of software developers, ML engineers, and data engineers. Create an environment that encourages innovation, collaboration, and continuous learning across both software engineering and  ML/ML Ops domains.
• Cross-functional Collaboration: Serve as a bridge between the ML/MLOps and software engineering teams, ensuring tight integration and collaboration. Work closely with product management, UX/UI designers, and other stakeholders to deliver a seamless, high-quality product.

• Experience: At least 5 years of technology experience, with experience in leadership roles managing teams that specialize in both ML/MLOps and core software engineering. Experience with ML metrics observability, workflow orchestration, service release automation, notebook development, and LLM deployment is a plus.
• Technical Expertise: A deep understanding of Enterprise Software architecture, design patterns, and modern programming languages coupled with a strong foundation in machine learning algorithms, data modeling, and MLOps practices across the major cloud providers (AWS, Azure, GCP).
• Leadership and Vision: Proven ability to lead, inspire, and grow multidisciplinary engineering teams. A Strategic thinker with the capacity to balance short-term goals with long-term vision.
• Collaborative Skills: Excellent communication and collaboration skills, capable of fostering positive relationships across engineering teams and with other business units.
• Education: Advanced degree in Computer Science, Engineering, or a related field, with a strong background in both AI/machine learning and software engineering.