Director, AI Platforms & Engineering
New
V
VSP VisionAI platforms
Source API remote eligibility restrictions: United StatesFull-TimeDirector
Salary105,000 - 165,375 USD per year
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Job Details
- Experience
- Minimum of 6 years of management experience; Minimum of 2 additional years of experience related to functional area.
- Required Skills
- AWSMachine LearningSnowflakeAzureGenerative AI
Requirements
- Bachelor’s degree in Computer Science, Engineering, Data Science, Information Systems, or a related field, or equivalent relevant experience; advanced degree preferred.
- At least 6 years of management experience, including hiring, training, assigning work, and managing direct-report performance.
- At least 2 additional years of experience related to the functional area.
- Experience across software, data, or machine-learning engineering, cloud platforms, or related disciplines, including 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, AWS, or Azure.
- Experience with LLM stacks, orchestration frameworks, MLOps/LLMOps, CI/CD, APIs, and observability.
- Experience defining enterprise architecture standards, reusable platform services, and a federated enablement model adopted across multiple teams.
- Knowledge of responsible AI, security, privacy, and governance in regulated environments, including auditability and access control.
- Experience managing platform budgets, strategic vendor commitments, and consumption/FinOps for scaled technology or AI environments.
- Strong executive communication and stakeholder-management skills, including translating technical tradeoffs into clear priorities and outcomes.
Responsibilities
- Own the multi-year strategy and reference architecture for AI platforms spanning the data platform, ML platform, agentic platform, and AI-enabled BI.
- Define how ML and agentic platforms build on the enterprise data platform and support BI and agent use cases.
- Establish architectures, standards, guidelines, reusable patterns, and build-buy-configure-partner decision frameworks for AI capabilities.
- Own and evolve enterprise AI platforms, services, frameworks, and APIs for model access, orchestration, knowledge retrieval, observability, identity, and security.
- Drive engineering practices for AI agents, intelligent workflows, APIs, and embedded AI experiences, including testing, CI/CD, versioning, release management, and production support.
- Establish lifecycle management, governance controls, safety controls, and production standards for models, generative AI applications, and agents.
- Ensure hosted solutions are governed, permissions-aware, auditable, and meet requirements for healthcare data, consumer information, and intellectual property.
- Own operational reliability, availability, and performance; forecast platform consumption and manage vendor commitments.
- Set team roles and performance expectations, and develop hiring, staffing, operating, and succession plans.
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