Define and communicate the long-term vision for ML/AI applications and engineering, aligning with corporate strategy.
Partner with senior executives, product, and engineering leaders to prioritize initiatives and allocate resources effectively.
Oversee design, development and deployment of enterprise-scale ML/AI solutions, inference pipelines, and respective technical roadmaps.
Ensure operational excellence in ML/LLM Ops, including automation, observability, and lifecycle management in partnership with the AIML platform engineering team.
Champion adoption of cutting-edge techniques (Deep learning, recommendation engine architecture, graph neural networks, causal inference, LLMs, RLs, etc). Drive responsible AI practices, model interpretability, and compliance with regulatory requirements.
Build and mentor a high-performing organization of managers and senior engineers. Foster a culture of continuous learning, experimentation, and engineering craftsmanship.
Establish standards for security, scalability, and architectural integrity across ML/AI systems. Implement robust governance for data privacy, ethical AI, and risk mitigation.

PhD in Computer Science, Statistics and related field with 10+ years in Machine Learning engineering (preferred); or MS with 12+ years of experience.
Minimum 5 years in senior leadership roles managing managers and large engineering teams.
Proven track record in building production-grade ML systems with strong problem-solving skills.
Hands-on experience building different types of models (ex: large-scale real-time Recommendation models, Causal Inference, Offer optimization, RL, multi-layer DL algorithms, Propensity, Churn, etc.) and scientific solutions.
Deep knowledge of ML lifecycle tools (MLflow, Kubeflow), distributed systems, and cloud-native architectures.
Ability to influence C-suite stakeholders and communicate complex technical concepts clearly.
Experience in strategic planning, budgeting, and organizational scaling.
Passion to solve problems and drive value-based transformative changes.
Experience building, training and developing engineering teams.

Director, Machine Learning Engineering

Key skills