Senior Director, Enterprise AI Platform Engineering - Hybrid (Acton, MA or San Diego, CA)

Insulet Corporation•San Diego, CA

2d•$225,000 - $337,500•Hybrid

About The Position

The Senior Director, Enterprise AI Platform Engineering will define and lead Insulet’s enterprise AI platform vision, strategy, architecture, and operating model. The role enables the scalable adoption of Microsoft Copilot, custom copilots, AI agents, knowledge retrieval, document intelligence, semantic intelligence, intelligent automation, and other AI-driven services across the organization. It has end-to-end accountability for AI platform engineering, operations, governance, architecture, observability, enablement, FinOps, and AI service delivery, shaping platform investments, enterprise standards, risk controls, and technology modernization priorities. As a senior leader in the Enterprise AI and Data organization, this role will lead a diverse, multi-layer organization of directors, senior managers, architects, engineers, product leaders, and technical specialists. The leader will work with executives and technology and business stakeholders, including at CTO and ELT levels, to align investment and architecture decisions with business priorities, risk expectations, and modernization roadmaps. The role is hybrid and requires on-site work from an Insulet office; listed locations are Acton, Massachusetts, or San Diego, California.

Requirements

Bachelor's or Master's degree in computer science, engineering, data science, information systems, analytics, or a related technical field.
18+ years of progressive experience leading enterprise-scale technology, data, analytics, AI, platform engineering, or cloud engineering organizations, including significant leadership of leaders and multidisciplinary teams.
Demonstrated experience setting multi-year enterprise platform strategy, owning complex investment portfolios, and delivering secure, reliable, reusable services at scale.
Strong understanding of generative AI, large language models, RAG, AI agents, copilots, embeddings, vector databases, semantic search, document intelligence, knowledge graphs, MLOps/LLMOps, APIs, and cloud-native engineering.
Demonstrated ability to influence executive stakeholders and communicate architecture choices, investments, value, and risk at CTO and ELT levels.

Nice To Haves

Experience in healthcare, medical devices, life sciences, diabetes care, digital health, or other regulated industries.
Hands-on experience with Microsoft 365 Copilot, Copilot Studio, Azure AI, Azure OpenAI, Databricks, Snowflake, Salesforce, ServiceNow, and enterprise integration ecosystems.
Experience building or operating AI FinOps, governance automation, AI observability, model gateways, prompt management, evaluation frameworks, or enterprise guardrail services.
Strong executive communication and influence skills, with the ability to translate complex platform choices into clear investments, operating models, and outcomes.

Responsibilities

Define and own a 5+ year enterprise AI platform vision, architecture strategy, capability roadmap, operating model, and investment plan aligned with company strategy and modernization priorities.
Set the enterprise roadmap for Microsoft Copilot, Copilot Studio, Azure AI, large language models, RAG, agents, document intelligence, semantic search, vector stores, orchestration frameworks, model gateways, and reusable AI services.
Establish enterprise decision frameworks, reference architectures, platform patterns, reusable blueprints, and engineering standards that balance speed, security, compliance, interoperability, performance, reliability, and cost.
Present platform strategy, investment recommendations, build-versus-buy decisions, vendor choices, value cases, and risk assessments to the CTO, ELT, and senior business and technology leaders.
Partner with executives and technology leaders to align AI platform investments with business priorities, risk expectations, and modernization roadmaps.
Own enterprise capabilities and the operating model for AI operations, LLMOps, platform reliability, AI enablement, developer experience, product onboarding, and production support.
Lead engineering patterns and reusable assets for Microsoft 365 Copilot, Copilot Studio, Teams-based assistants, custom copilots, role-based business assistants, and enterprise AI agents embedded in workflows.
Establish enterprise enablement services, including onboarding, reference implementations, prompt and agent libraries, connectors, integration adapters, evaluation harnesses, playbooks, and communities of practice.
Drive adoption across Commercial, Customer Service, Finance, Supply Chain, Product, Quality, Regulatory, R&D, and enterprise functions while reducing fragmented or duplicative solutions.
Own enterprise knowledge retrieval, document intelligence, and semantic architecture, including ingestion, metadata, access-aware retrieval, vector indexing, source attribution, citation quality, and lifecycle standards.
Scale reusable document intelligence capabilities for classification, OCR, extraction, summarization, search, automation, and unstructured data processing.
Establish semantic intelligence capabilities, including business glossaries, ontologies, semantic models, metadata catalogs, knowledge graphs, domain context layers, and reusable definitions.
Define enterprise data-readiness standards for AI, including authoritative sources, permissions, lineage, freshness, retention, quality thresholds, and human validation.
Own governance automation and policy-as-code capabilities that embed responsible AI, privacy, security, quality, and compliance controls into the platform lifecycle.
Establish enterprise AI FinOps, including consumption visibility, cost allocation, forecasting, capacity planning, model routing, caching, and cost-to-value reporting.
Set LLMOps and AI observability standards for model and prompt performance, retrieval quality, hallucination risk, citation accuracy, latency, usage, incidents, user feedback, and production reliability.
Partner with Security, Privacy, Legal, Compliance, Finance, Quality, Regulatory, and Data Governance to provide the CTO and ELT with platform risk assessments, control effectiveness, and remediation priorities.
Own a portfolio of reusable AI platform services, APIs, connectors, prompt modules, agent frameworks, model gateways, evaluation tools, monitoring capabilities, and semantic services.
Set enterprise engineering standards for versioning, testing, CI/CD, deployment, monitoring, documentation, incident response, lifecycle management, resilience, and retirement.
Establish portfolio governance, funding priorities, delivery mechanisms, service levels, adoption measures, and value realization across platform capabilities.
Drive enterprise adoption of common platforms and reusable services, reducing duplicate builds, fragmentation, technical debt, and time from experimentation to trusted production.
Build, lead, and develop a multi-layer, diverse organization of directors, senior managers, architects, engineers, product leaders, and technical specialists accountable for enterprise AI platform outcomes.
Define the platform organization design, talent strategy, workforce plan, leadership structure, and succession pipeline required to scale enterprise capabilities.
Serve as the enterprise thought leader for AI platform engineering and influence technology strategy, architecture, investments, vendor decisions, risk posture, and modernization priorities at CTO and ELT levels.
Provide formal leadership through direct management and enterprise leadership through influence across Technology, Cybersecurity, Data and Analytics, Product Development, R&D, Quality, Regulatory, Commercial, Digital, and Operations.