We are seeking a visionary and execution-oriented Director of AI Engineering to join our team. In this senior, client facing role, you will own the full lifecycle of AI model development, setting technical strategy and ensuring that cutting-edge machine learning solutions move from concept to production with business impact at their core.
With roots in data science and hands-on expertise in custom transformer architecture, you will bring both the credibility to lead technical teams. You will operate at the intersection of stakeholder management and deep technical execution and you should be equally comfortable presenting to a senior audience and reviewing model architecture with your team.
This is a high-impact, high-autonomyy role with significant organizational influence. You will define the AI roadmap, establish engineering best practices, and champion a culture of rigorous, reproducible, and responsible machine learning.
Strategic Leadership & Team Management
- Define technical investments with business objectives
- Mentor, and manage AI/ML engineers, senior data scientists, and MLOps engineers—setting performance expectations and a high-performance culture.
- Partner with cross-functional leaders to prioritize initiatives, allocate resources, and measure organizational impact.
- Establish engineering standards, code review practices, and model governance frameworks across the AI org.
Custom Transformer Architecture & Model Development
- Serve as the technical authority on deep learning architecture—personally leading the design and development of custom transformer models for sequence modeling, customer propensity scoring, audience segmentation, and churn prediction.
- Drive innovation in attention mechanisms, positional encodings, and tokenization strategies specifically suited to tabular, time-series, and event-stream data common in marketing and telecom.
- Oversee adaptation and fine-tuning of foundation models (BERT, T5, TabTransformer, LLMs) for proprietary client datasets, ensuring domain-specific performance.
- Champion reproducible experimentation and architectural decision documentation across the team.
Data Science & Applied Analytics
- Oversee end-to-end data science workflows: problem framing, feature engineering, model development, validation, and production deployment.
- Ensure statistical rigor in experimental design, causal inference, A/B testing, and offline/online evaluation frameworks.
- Guide the team in building robust data pipelines for large-scale structured and unstructured datasets, including clickstream, CRM, ad telemetry, CDRs, and network KPIs.
Client & Executive Engagement
- Lead technical discovery and solutioning with enterprise clients translating ambiguous business problems into well-scoped AI initiatives.
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