Director, AI Platforms & Engineering

The Director, AI Platforms & Engineering defines and leads the organization’s enterprise AI platform strategy, architecture, and engineering capabilities. This role is accountable for the platforms, standards, and governance that enable the secure, reliable, and scalable development, deployment, and operation of AI solutions across the organization. Responsible for machine learning, agentic AI, and AI-enabled business intelligence platforms, the role provides a common AI foundation that supports innovation while maintaining security, compliance, reliability, and cost efficiency.

Own the multi-year strategy and reference architecture for the organization’s AI platforms, spanning the data platform, ML platform, agentic platform, and AI-enabled BI

Define how the ML and agentic platforms sit on top of the enterprise data platform, and how ML products support BI and agent use cases, to present a coherent platform and capabilities journey

Establish a scalable architecture for generative AI, AI agents, traditional machine learning, computer vision, and emerging AI technologies that preserves optionality across cloud providers, foundation models, and SaaS platforms

Set enterprise AI architecture standards, environment standards, tool/model selection guidelines, and reusable patterns in partnership with Enterprise Architecture

Establish clear decision frameworks for when the organization should build, buy, configure, or partner for AI capabilities

Own and evolve enterprise AI platforms, services, frameworks, and APIs that enable scalable AI development and operations, including model access, orchestration, knowledge retrieval, observability, identity, and security

Establish engineering standards for AI agents, intelligent workflows, APIs, and embedded AI experiences

Drive disciplined software engineering practices including automated testing, CI/CD, versioning, release management, and production support

Establish enterprise lifecycle management for predictive models, generative AI applications, and autonomous or semi-autonomous agents

Translate organization’s AI governance policies into practical technical controls embedded within the platform

In partnership with various stakeholders, ensure platform-hosted solutions are governed, permissions-aware, and auditable, and meet requirements for healthcare data, consumer information, and intellectual property

Implement platform-level safety controls proportionate to risk

Establish production standards and service-level expectations for enterprise AI capabilities, and own operational reliability, availability, and performance

Forecast and manage platform consumption and vendor commitments (e.g., Snowflake/Cortex, cloud, and model usage)

Establish clear roles, decision rights, performance expectations, development plans, and succession readiness; align replacement and future hiring to evolving organizational needs

Develop annual and quarterly operating plans covering priorities, capacity, staffing, funding, vendor needs, technology consumption, and anticipated outcomes

Job Specifications Typically Has The Following Skills Or Abilities Bachelor’s degree in Computer Science, Engineering, Data Science, Information Systems, or a related field, or equivalent relevant experience; advanced degree preferred; Minimum of 2 additional years of experience related to functional area

Minimum of 6 years of management experience with responsibilities for hiring, training, assigning work, and managing performance of direct reports

Experience across software, data, or machine-learning engineering, cloud platforms, or related disciplines, including building and operating production platforms

Demonstrated experience taking machine learning or generative AI capabilities into production at enterprise scale, with strong understanding of modern GenAI architectures, foundation models, RAG, AI agents, and vector technologies

Experience delivering and operating cloud data and AI platforms (e.g., Snowflake, AWS, Azure) and modern tooling including LLM stacks, orchestration frameworks, MLOps/LLMOps, CI/CD, APIs, and observability

Proven ability to define enterprise architecture standards, reusable platform services, and a federated enablement model adopted across multiple teams

Strong grounding in responsible AI, security, privacy, and governance for regulated envir…