Director, AI Engineering (Data Science)

We are seeking a visionary and execution-oriented Director of AI Engineering to join our 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 engineerssetting 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 architecturepersonally 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.
• Present AI strategy model results and roadmap updates to C-suite and senior client stakeholders with clarity and executive presence.
• Contribute to business development: support RFP responses lead technical portions of client proposals and help grow the AI engineering practice.

MLOps Infrastructure & Governance

• Establish production standards for model deployment monitoring drift detection and automated retraining across cloud platforms (AWS SageMaker GCP Vertex AI Azure ML).
• Drive adoption of MLOps best practices including CI/CD for ML containerization (Docker/Kubernetes) and experiment tracking (MLflow W&B DVC).
• Implement model governance explainability and responsible AI standards in compliance with client and regulatory requirements.

Qualifications :

• Bachelors or Masters degree in Computer Science Statistics Mathematics or a closely related quantitative field; Ph.D. strongly preferred.
• 10 years of progressive experience in data science and machine learning with at least 35 years in a people management or technical leadership role (Director Sr. Manager or Principal Engineer level).
• Proven track record of  leading high-performing AI/ML engineering teams in a fast-paced client-facing or product environment.
• Deep hands-on expertise designing and training custom transformer architectures from scratchnot only fine-tuning pre-built checkpoints but architecting novel attention mechanisms embedding strategies and model topologies.
• Strong applied data science foundation: feature engineering statistical modeling causal inference and experimental design across large-scale datasets.
• Proficiency in Python and core ML/DL libraries: PyTorch (preferred) TensorFlow HuggingFace Transformers scikit-learn XGBoost/LightGBM.
• Direct experience with industry datasets in marketing & media (DSP/DMP logs ad impression data attribution pipelines MMM) OR telecommunications (CDRs network KPIs subscriber behavior churn datasets).
• Command of SQL and large-scale data platforms: Spark BigQuery Snowflake or Databricks.
• Experience owning end-to-end MLOps: cloud deployment (SageMaker Vertex AI or Azure ML) monitoring CI/CD for ML and model governance.
• Exceptional executive communication skillsable to translate complex model behavior into business language for C-suite and client audiences.

PREFERRED QUALIFICATIONS

• Professional services experience across multiple client engagements or business units
• Background in privacy-preserving ML: federated learning differential privacy or synthetic data generationespecially relevant in post-cookie marketing environments.
• Knowledge of graph neural networks (GNNs) for social graph or network topology analysis in telecom contexts.
• Published research or conference contributions (NeurIPS ICML KDD RecSys or industry equivalents) related to applied transformers tabular deep learning or domain-specific AI.
• Experience with real-time inference and streaming ML pipelines (Kafka Flink or similar).
• Demonstrated ability to build strategic partnerships with external clients contributing to revenue growth or account expansion through technical leadership.
• Deep experience with openai focused on embeddings
• Experience building custom transformer models

Remote Work :

Yes

Employment Type :

Full-time