Strategy and Execution - Platforms - AI ML Engineering - Director

Location:

Atlanta, Boston, Chicago, Dallas, Denver, Detroit, Houston, Los Angeles, McLean, New York, Hoboken, Philadelphia, San Francisco, Seattle

Within the EY-Parthenon service line, the EY Growth Platforms AI ML Engineering Director will collaborate with Business Leaders, Data Scientists, Project Managers, and other team members to design, build, and scale innovative AI solutions that power strategic growth initiatives and create enterprise value for F500 clients.

• The EY Growth Platforms AI ML Engineering Director will play a critical role building and maintaining our core advanced analytics platform and serving the technical execution lead for high-visibility client engagements.
• You’ll work with Business leaders and C-level executives to translate business needs into technically executable ML agentic workflows and guide a high-performing team of engineers and contractors through end-to-end project life cycles.

• Partner with Business and Strategy Leads to translate business needs into executable AI workflows, data pipelines, and client-specific product specifications.
• Define the end-to-end architecture for agents that integrate LLMs, retrieval-augmented generation (RAG), multi-source data ingestion, and analytics components.
• Lead model selection, feature engineering, embedding strategy, and prompt frameworks (e.g., Lang Chain, Llama Index).
• Design and build robust data pipelines that are scalable, reproducible, and versioned.
• Collaborate with Data Engineering and Dev Ops on pipeline integration, data normalization, and system deployment.
• Set coding standards and mentor junior engineers; perform model/code reviews and LOE, scope and roadmap refinement.
• Own delivery of AI/ML-powered components from prototype to production (REST APIs, dashboards, or embedded agents).

• A bachelor’s degree in Business, Statistics, Economics, Mathematics, Engineering, Computer Science, Analytics, or other related field and 5 years of related work experience; or a graduate degree and approximately 3 years of related work experience.
• Full-time, hands‑on experience applying AI/ML to solve real‑world problems.
• Stong experience using the following ML models: XGBoost, Logistics Regression or similar.
• Strong understanding of real‑world structured data merging, schema linking, and model evaluation at scale.
• Strong understanding of ML workflow including ingesting, analyzing, transforming data, and evaluating results to make meaningful predictions.
• Fluency in Python, PyTorch or Tensor Flow, with ability to architect APIs around ML models.
• Proven experience designing, building, and maintaining ML models, frameworks, and pipelines.
• Experience working with and/or leading cross‑functional business/engineering teams to deliver complex solutions.
• Excellent communication skills, with the ability to convey complex, technical concepts and progress, methodologies, solutioning, and results to business and client stakeholders.
• The ability and willingness to travel and work in excess of standard hours when necessary.

• Experience building and deploying AI/ML products or features in a production environment.
• Experience working in a startup and/or management/strategy consulting.
• Knowledge of how to leverage AI tools in a business setting, including Microsoft Copilot.
• Collaborative, problem‑solving, and growth‑oriented mindset.

• We offer a comprehensive compensation and benefits package where you’ll be rewarded based on your performance and recognized for the value you bring to the business. The base salary range for this job in all geographic locations in the US is $205,000 to $235,000. Individual salaries within those ranges are determined through a wide variety of factors including but not limited to education, experience, knowledge, skills and geography.
  
  In addition, our Total Rewards package includes medical and dental coverage, pension and 401(k) plans, and a wide range of paid time off options.
• Join us in our team‑led and leader‑enabled hybrid model. Our expectation is for most people in…