Director, AI Platforms – Engineering

VSP Vision Care

. Define and lead the organization’s enterprise AI platform strategy, architecture, and engineering capabilities .

Posted 10/23/2026full-timeRemote • United StatesLead💰 $105,000 - $165,375 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in defining and leading enterprise AI platform strategies, architectures, and engineering capabilities, with a strong focus on machine learning, generative AI, and cloud platforms. Proven ability to establish governance, compliance, and performance standards while managing platform budgets and vendor commitments.

Highest-signal resume keywords

Enterprise AI Platform StrategyMachine Learning EngineeringCloud Data Platforms (Snowflake, AWS/Azure)Responsible AI GovernanceExecutive Communication

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningGenerative AIAI ArchitectureCI/CDAPIsMLOps/LLMOpsData EngineeringObservabilityProduction Platform ManagementCloud Platforms

Soft Skills

Stakeholder ManagementTeam LeadershipPerformance ManagementCommunicationVirtual Engagement

Tools & Technologies

SnowflakeAWSAzureOrchestration FrameworksVersion Control SystemsKnowledge Retrieval SystemsIdentity Management ToolsSecurity ControlsAutomated Testing ToolsGenerative AI Applications

Industry Keywords

AI GovernanceHealthcare Data ComplianceIntellectual Property RequirementsData PrivacyFinOps

Tech Stack

Tools & technologies

AWSAzureCloud

About the role

Key responsibilities & impact

Define and lead the organization’s enterprise AI platform strategy, architecture, and engineering capabilities
Own the multi-year strategy and reference architecture for data, ML, agentic, and AI-enabled BI platforms
Define how ML and agentic platforms integrate with the enterprise data platform and support BI and agent use cases
Establish scalable architectures for generative AI, AI agents, traditional machine learning, computer vision, and emerging AI technologies
Set enterprise AI architecture standards, environment standards, tool/model selection guidelines, and reusable patterns
Establish decision frameworks for building, buying, configuring, or partnering for AI capabilities
Own and evolve enterprise AI platforms, services, frameworks, and APIs, including model access, orchestration, knowledge retrieval, observability, identity, and security
Establish engineering standards for AI agents, intelligent workflows, APIs, and embedded AI experiences
Drive automated testing, CI/CD, versioning, release management, and production support
Establish lifecycle management for predictive models, generative AI applications, and autonomous or semi-autonomous agents
Translate AI governance policies into practical technical controls
Ensure platform-hosted solutions are governed, permissions-aware, auditable, and compliant with healthcare data, consumer information, and intellectual property requirements
Implement platform-level safety controls proportionate to risk
Establish production standards and service-level expectations; own reliability, availability, and performance
Forecast and manage platform consumption and vendor commitments, including Snowflake/Cortex, cloud, and model usage
Establish roles, decision rights, performance expectations, development plans, and succession readiness
Develop annual and quarterly operating plans covering priorities, capacity, staffing, funding, vendors, technology consumption, and outcomes

Requirements

What you’ll need

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 the functional area
Minimum of 6 years of management experience, including hiring, training, assigning work, and managing performance of direct reports
Experience across software, data, or machine-learning engineering, cloud platforms, or related disciplines
Experience building and operating production platforms
Experience taking machine learning or generative AI capabilities into production at enterprise scale
Strong understanding of modern GenAI architectures, foundation models, RAG, AI agents, and vector technologies
Experience delivering and operating cloud data and AI platforms, such as Snowflake and AWS/Azure
Experience with LLM stacks, orchestration frameworks, MLOps/LLMOps, CI/CD, APIs, and observability
Experience defining enterprise architecture standards, reusable platform services, and federated enablement models
Strong grounding in responsible AI, security, privacy, and governance for regulated environments
Experience managing platform budgets, strategic vendor commitments, and consumption/FinOps
Strong executive communication and stakeholder-management skills
For remote or hybrid roles, high engagement in virtual environments, including maintaining camera presence during meetings
Must comply with information security and privacy policies
Subject to pre-employment substance abuse testing

Benefits

Comp & perks

Eligible bonuses and commissions may be included as part of the compensation package
VSP Vision benefits are available (specific benefits linked in posting)
Remote work arrangement