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

Job Summary

Senior Director, Enterprise AI Platform Engineering

The Senior Director, Enterprise AI Platform Engineering will define and lead Insulet’s enterprise AI platform vision, strategy, and architecture, enabling the scalable adoption of Microsoft Copilot, custom copilots, AI agents, intelligent automation, and advanced AI-driven services across the organization. This leader will own end-to-end accountability for AI platform engineering, operations, governance, architecture, observability, and AI service delivery, driving enterprise standards, risk management, and technology modernization.

The ideal candidate is a strategic enterprise leader who can influence at the executive level while building and leading high-performing, multidisciplinary teams that translate AI innovation into measurable business value.

 Senior Director, Enterprise AI Platform Engineering

The Senior Director, Enterprise AI Platform Engineering will set the enterprise vision, strategy, architecture, and operating model for Insulet's AI platform ecosystem. This role will own the capabilities required to safely scale Microsoft Copilot, custom copilots, AI agents, knowledge retrieval, document intelligence, semantic intelligence, and AI-powered workflow automation across the enterprise.

As a senior leader within the Enterprise AI and Data organization, this role will have end-to-end accountability across AI platform engineering, AI operations, governance automation, enterprise AI architecture, enablement, FinOps, observability, and reusable AI services. The role will shape long-term platform investments, enterprise standards, risk controls, and technology modernization priorities.

The ideal candidate is an enterprise platform leader who can operate at CTO and ELT levels while building and leading a multi-layer, diverse organization of directors, senior managers, architects, engineers, product leaders, and technical specialists.

Key Responsibilities

1. Enterprise AI Platform Strategy, Architecture, and Investment

Define and own a 5+ year enterprise AI platform vision, architecture strategy, capability roadmap, operating model, and investment plan aligned with company strategy and technology modernization priorities.

Set the enterprise roadmap across 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 enterprise executives and technology leaders to align AI platform investments with business priorities, risk expectations, and broader modernization roadmaps.

2. AI Operations, Copilot, Agentic AI, and Enterprise Enablement

Own the enterprise capabilities and 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 into 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.

3. Knowledge, Document, and Semantic Intelligence Platforms

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.

4. Governance Automation, Responsible AI Operations, FinOps, and Risk

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.

5. Engineering Excellence and Enterprise Delivery

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.

6. Multi-Layer Organizational Leadership and Enterprise Influence

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.

Required Qualifications

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 multi-disciplinary 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 in