Sr. Director, AI Platform and Engineering

Sr. Director, Ai Platform And Engineering

As the Sr. Director, AI Platform and Engineering, you will play a key role in supporting The Mutual Group (TMG), GuideOne Insurance, and future members by leading the technical design, engineering practices, and execution required to turn TMG's AI-First IT vision into practical enterprise capability. This role is accountable for building reusable AI platforms, architecture patterns, engineering playbooks, and delivery practices that enable AI adoption across business processes, software delivery, and IT operations.

This is a highly technical leadership role for a hands-on engineering leader who can operate across AI platforms, application engineering, automation, data, cloud, integration, security, observability, and modern software delivery. The Sr. Director will lead a small team of architects, engineers, contractors, and system integration partners to build foundational capabilities and reusable assets that help TMG move from AI experimentation to production-grade implementation.

The role will work closely with Applications, Engineering, Infrastructure, Operations, Security, Data, Architecture, business teams, and AI & Technology Risk Governance to ensure AI capabilities are secure, governed, observable, reliable, cost-effective, and ready to scale. The team is intended to serve as a catalyst, creating platforms, patterns, and playbooks that broader IT and business teams can adopt over time.

Work Arrangement:

• Employees who live within 30 miles of the TMG home office are expected to follow a hybrid or in-office schedule. The initial training period may require additional in-office days.

Accountabilities:

Platform Strategy & Technical Leadership

• Define and lead the technical roadmap for TMG's enterprise AI platform and AI engineering capabilities, aligned to business priorities, enterprise architecture, security standards, and governance expectations.
• Translate AI-First IT strategy into practical platform capabilities, reference architectures, engineering standards, reusable components, and delivery patterns.
• Evaluate AI platforms, cloud services, frameworks, vendor solutions, integration patterns, and development tools with a focus on security, reuse, interoperability, scalability, maintainability, and business value.
• Provide hands-on technical leadership in architecture reviews, solution design, technical decision-making, delivery planning, and complex problem-solving.
• Stay current on emerging AI engineering patterns, GenAI platforms, agent frameworks, model orchestration, enterprise knowledge systems, and responsible deployment practices.

Business and Technology Enablement

• Partner with business and technology teams to design and deliver AI-enabled capabilities for underwriting, claims, operations, finance, customer service, and other enterprise functions.
• Translate business use cases into scalable technical solutions, including decision support, workflow automation, document intelligence, knowledge retrieval, summarization, classification, triage, and productivity tools.
• Establish repeatable technical patterns for moving AI use cases from proof of concept to secure, production-ready adoption.
• Work with product, business, data, and risk partners to define solution feasibility, data needs, integration approach, human oversight, evaluation criteria, and operational readiness.
• Build reusable accelerators and implementation playbooks that allow similar AI capabilities to be deployed across multiple business processes with less rework.

Architecture, Engineering Standards & Platform Delivery

• Define architecture patterns for AI-enabled applications, copilots, intelligent workflows, automation agents, enterprise knowledge solutions, and reusable AI components.
• Establish technical standards for model access, prompt and response handling, context management, retrieval-augmented generation, semantic search, vector databases, observability, cost management, and production support.
• Lead delivery of foundational AI platform capabilities such as model gateways, model catalogs, orchestration layers, RAG frameworks, vector stores, embedding pipelines, evaluation frameworks, usage monitoring, and governance controls.
• Establish patterns for integrating AI capabilities with enterprise systems, APIs, data platforms, document repositories, workflow tools, service management platforms, and business applications.
• Create reusable engineering assets, templates, reference implementations, and deployment playbooks that improve delivery speed, quality, consistency, and reuse.

GenAI, Agentic AI & AI Engineering Practices

• Guide implementation of Generative AI solutions using LLMs, SLMs, embeddings, prompt engineering, RAG, semantic search, summarization, classification, extraction, and enterprise knowledge retrieval.
• Define patterns for Agentic AI, including tool and function calling, workflow orchestration, human-in-the-loop controls, memory and context management, guardrails, monitoring, and safe execution.
• Establish usage patterns for Model Context Protocol (MCP) or similar approaches for connecting AI systems to enterprise tools, data sources, APIs, and workflow actions in a secure and governed manner.
• Guide AI-assisted engineering practices across coding, testing, documentation, requirements analysis, code review, quality engineering, and developer productivity.
• Collaborate with Infrastructure and IT Operations teams to apply AI to observability, incident response, root cause analysis, predictive monitoring, runbook automation, service management, and operational productivity.

Security, Governance & Production Readiness

• Partner with the Senior Director, AI & Technology Risk Governance, Security, Legal, Compliance, Data, and Architecture teams to operationalize AI engineering governance.
• Embed secure-by-design and privacy-by-design principles into AI platform architecture, data flows, integrations, engineering practices, and production deployments.
• Define engineering controls for sensitive data handling, identity and access management, prompt and response logging, output validation, human oversight, vendor integration, and production readiness.
• Ensure AI platforms and applications are designed with appropriate logging, monitoring, traceability, testing, resilience, access controls, incident response, and lifecycle management.
• Establish practices for model selection, experimentation, evaluation, validation, performance monitoring, drift detection, feedback loops, cost optimization, and responsible production deployment.

Team Leadership, Delivery & Enterprise Enablement

• Lead and develop a high-performing technical team of architects, engineers, contractors, and integration partners.
• Lead from the front by staying close to the work, reviewing designs, resolving technical issues, and personally driving complex initiatives when needed.
• Own delivery across scope, schedule, quality, risk, dependencies, technical readiness, adoption, and business value.
• Build communities of practice, technical playbooks, reference architectures, and enablement materials that help broader IT teams adopt AI-First engineering practices over time.
• Create a culture of engineering excellence, accountability, curiosity, disciplined experimentation, documentation, reuse, security, and continuous improvement.

Qualifications:

• 12+ years of progressive technology experience, including leadership responsibility for software engineering, platform engineering, architecture, cloud, data, automation, AI, or enterprise technology delivery.
• 8+ years of experience with AI, machine learning, automation, advanced analytics, intelligent platforms, developer productivity tools, or emerging technology capabilities.
• Strong technical depth in Generative AI patterns, including LLMs, SLMs, embeddings, prompt engineering, RAG, vector databases, semantic search, evaluation frameworks, and enterprise knowledge integration.
• Experience with Agentic AI patterns, including agents, tool/function calling, orchestration, human-in-the-loop workflows, context management, guardrails, monitoring, and safe deployment.
• Familiarity with Model Context Protocol (MCP) or similar approaches for securely connecting AI systems to enterprise tools, data sources, APIs, and workflow actions.
• Strong technical fluency across cloud platforms, APIs, microservices, event-driven architecture, data platforms, DevSecOps, CI/CD, test automation, observability, cybersecurity, identity, and privacy.
• Proven experience delivering production-grade enterprise platforms, reusable engineering frameworks, integration patterns, automation capabilities, or developer productivity solutions.
• Experience working in regulated environments with strong security, privacy, risk, compliance, auditability, and operational readiness expectations.
• Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science, or related field required. Master's degree preferred.

Leadership Attributes

• Deeply technical, hands-on engineering leader who can guide architecture and personally drive complex problem-solving when needed.
• Practical platform and delivery leader with strong bias for reusable, scalable, secure, and maintainable solutions.
• Strong analytical thinker who uses data, metrics, and root-cause analysis to improve engineering and operational outcomes.
• Collaborative partner who can work across business, technology, security, data, infrastructure, architecture, operations, and governance teams.
• Strong delivery owner with disciplined