Senior Director, AI Engineering

Responsibilities

• Lead and Scale the MLE Organization
• Build, lead, and mentor a global team of Machine Learning Engineers and technical leaders
• Establish a high-performance engineering culture focused on quality, velocity, and accountability
• Deliver Production-Grade AI/ML Systems
• Own end-to-end delivery of ML platforms, pipelines, and services (training, inference, monitoring)
• Operationalize models into scalable, reliable, and secure production systems
• Partner with Data Science and Product to move from experimentation to deployment
• Define AI Engineering Strategy & Architecture
• Set the vision for ML platform architecture, MLOps, and GenAI enablement
• Standardize tools, frameworks, and best practices for model development and deployment
• Ensure systems are built for scale, performance, and cost efficiency
• Lead Development of GenAI Capabilities
• Enable reusable AI services and APIs to accelerate use case delivery
• Stay ahead of industry trends and translate them into enterprise-ready capabilities
• Cross-Functional Leadership & Stakeholder Alignment
• Partner with Product, Data, Engineering, and Business leaders to prioritize high-impact use cases
• Communicate strategy, progress, and outcomes to executive stakeholders
• Align AI initiatives with business goals, including revenue growth, efficiency, and customer experience
• Governance, Risk, and Responsible AI
• Establish best practices for model governance, monitoring, and lifecycle management
• Ensure compliance with security, privacy, and ethical AI standards
• Implement guardrails for safe and responsible use of AI technologies

Qualifications

• 12–15+ years in software engineering, data engineering, or ML engineering
• 5+ years leading large, distributed engineering teams (including managers of managers)
• Proven track record of delivering ML/AI systems at scale in production environments
• Deep knowledge of machine learning systems, MLOps, and cloud-native architectures
• Experience with ML frameworks (e.g., TensorFlow, PyTorch) and data platforms
• Strong understanding of GenAI/LLMs, prompt engineering, and retrieval-augmented systems
• Familiarity with distributed systems, APIs, and microservices architecture
• Strong ability to translate business strategy into technical execution
• Excellent communication and stakeholder management skills

Preferred

• Experience building enterprise AI platforms or internal AI products
• Experience in global delivery models (e.g., US + India engineering hubs)
• Master’s or PhD in Computer Science, Engineering, or related field