Engineering Delivery & Leadership
* Own all engineering deliverables — quality, scalability, and on-time execution
* Lead and manage the full engineering organization: AI Engineers, ML Engineers, MLOps, and Data Engineers
* Establish and run Agile/Scrum processes: sprints, backlog grooming, ticketing, and release planning
* Build a culture of accountability, experimentation, and continuous improvement
AI/ML Strategy & Architecture
* Define and standardize best practices across the full AI/ML lifecycle — from experimentation through deployment and monitoring
* Architect and oversee a full ML platform: infrastructure, scalability, reliability, and rapid iteration
* Design and build scalable, end-to-end ML solutions for predictions, recommendations, search, and growth systems
* Evaluate experiments, document findings, select winning approaches, and roll them out team-wide
* Contribute directly to system architecture and key technical decisions
* Oversee model training, optimization, deployment, and performance monitoring
* Drive data excellence: clean pipelines, strong governance, and actionable insights
* Establish frameworks that integrate generative AI into engineering workflows
* Join client calls as a senior technical voice, shaping solutions and building stakeholder trust
* Partner with product managers and business leaders to translate business objectives into technical roadmaps
* Present trade-offs clearly so clients understand what’s being built and why it will move their business
* Hire, coach, and mentor top-tier AI-first engineers and engineering leaders
* Establish clear career paths, feedback loops, and performance standards
* Scale the AI function across MLE, MLOps, and Data Engineering as the company grows