Director, AI Enterprise Engineering
Our client is a well-established, PE-backed technology company with operations spanning North America, Europe, and Asia. They serve a global customer base through a portfolio of software platforms, financial tools, and operational services. The engineering organization is undergoing a full transformation to an agentic, AI-driven delivery model, and this role sits at the center of executing that shift.
 

Position Summary

As Director, AI Enterprise Engineering, you drive the adoption of modern AI engineering practices across the software engineering organization, converting the company’s investment in agentic development tooling into repeatable, governed, and measurable engineering outcomes. This is a hands-on engineering leadership role reporting to the SVP of Engineering.

You will directly manage a distributed team of solution architects, engineers, and verification architects spanning onshore and offshore operations. You own hiring, coaching, performance management, and the operating cadence that keeps an agentic, spec-driven delivery model moving in tight build-measure-learn cycles.

The right candidate combines strong engineering fundamentals, modern Microsoft-stack delivery experience, hands-on agentic AI and multi-agent systems experience, and proven change leadership. Success is measured by outcomes: what the team ships, its reliability and business impact, adoption maturity, and the trust the business places in engineering.
 

Responsibilities

Engineering Strategy and Delivery

• Define and execute the team’s delivery roadmap, using agentic development and AI coding tooling as core capabilities, partnering with Product and the business to prioritize and evolve applications over time.
• Embed AI across the full delivery cycle: spec-driven intent, agent execution (plan, build, and test), automated verification quality gates, and production insight that feeds learnings back into the context store, while engineers remain accountable for intent, architecture, quality, and release decisions.
• Hold final accountability for the reliability, accuracy, and business impact of what the team ships, including AI agents, LLM-powered features, and automation built into applications in scope.
• Partner with peer engineering and architecture leaders to align on shared standards, roadmap, and reusable patterns.
• Stay close enough to the work to prototype, review examples, challenge assumptions, and demonstrate credible hands-on engineering judgment.

Engineering Leadership and People Management

• Directly manage a high-performing engineering team, owning hiring, onboarding, performance management, and individual career development.
• Run a single operating model across onshore and offshore delivery capability, with time-zone-aware rituals and communication practices that make a global team operate as one.
• Coach Solution Architects to set direction and mentor the rest of the team. Build collective AI engineering capability through structured learning paths, enablement sessions, champions, office hours, and communities of practice.
• Set direction collaboratively: frame options, integrate team input, commit to clear objectives and owners, and drive them to completion.

AI Tooling Enablement, Standards, and Governance

• Drive responsible enterprise adoption of AI coding tooling — onboarding, usage patterns, repository context, guardrails, training, and coaching — measured by meaningful workflow usage.
• Create and scale reusable engineering assets: skills, prompt libraries, MCP and context-sharing patterns, testing patterns, API standards, architecture decision records, and secure coding standards.
• Establish governance for AI-driven development — code ownership, review expectations, IP and secrets protection, auditability, approved model usage, and human-in-the-loop accountability — in partnership with Security, Legal, and Infrastructure.
• Ensure responsible AI in everything that ships: fairness, explainability, model monitoring, security posture, and regulatory alignment.

Enterprise Integration and Platform Alignment

• Ensure the team’s AI systems and automation integrate reliably with enterprise platforms including CRM, ERP, data platforms, and proprietary products, coordinating with data and platform teams.
• Standardize how the team builds and consumes MCP servers that expose enterprise systems as model-ready tools.
• Establish observability and lifecycle management standards that keep production automations dependable at scale.
• Influence engineering standards for API design, agent-based automated testing, code quality, deployment readiness, and architecture decision documentation.

Metrics, Stakeholders, and Executive Communication

• Define and report outcome-based metrics: cycle time, PR throughput, review latency, test coverage, escaped defects, deployment frequency, change failure rate, developer satisfaction, and token/cost governance.
• Track AI adoption and engineering proficiency over time: define an adoption maturity model, measure depth of AI tooling usage, baseline proficiency by individual and team, and use the data to target enablement where it moves the needle.
• Build trusted relationships across Engineering, Product, Architecture, Security, Infrastructure, Data, Finance, Sales, and Operations.
• Evaluate AI engineering capabilities and vendors in a fast-changing market, recommending what fits the company’s Azure/Microsoft environment and enterprise risk posture.
• Translate progress, risks, adoption, investment needs, and business impact into clear executive-level narratives and operating reviews.

Required Qualifications

Core Experience

• 10+ years delivering production software in enterprise or product-led environments, including 5+ years leading engineering teams and distributed or global teams.
• Recent, hands-on AI delivery: production systems shipped with measurable outcomes, not prototypes. Claude Code strongly preferred.
• Microsoft-stack fluency: C#, .NET/ASP.NET Core, REST APIs, React/TypeScript, SQL Server/Azure SQL, cloud-native patterns, and secure CI/CD.
• Experience leading engineering transformation, developer enablement, or SDLC modernization across multiple teams.
• Experience implementing governance, guardrails, and secure development practices in enterprise environments.
• Proven ability to define metrics and connect engineering practices to business outcomes.
• Excellent stakeholder and project management skills; able to explain engineering and AI concepts to executives and technical audiences alike.
• Strong judgment balancing speed, quality, security, cost, and AI adoption.

AI Ecosystem Proficiency (Claude Strongly Preferred)

• Hands-on experience with AI coding agents and agentic workflows — Claude Code strongly preferred; GitHub Copilot, Codex, or equivalent acceptable.
• Experience creating reusable AI assets: skills, agents, prompt libraries, workflow templates, MCP/context patterns, and codebase-specific instructions.
• Working knowledge of LLM APIs (Claude API preferred): tool use, structured outputs, document processing, streaming, and rate limits.
• Understanding of AI evaluation frameworks (quality, cost, latency) and responsible AI design.

Highly Desired Qualifications

• DORA metrics, SPACE concepts, and developer productivity telemetry or dashboards.
• Azure DevOps, GitHub, PR governance, SonarQube or similar quality tooling, and secure software supply chain practices.
• Integrations with Dynamics 365 F&O, Salesforce or equivalent CRM, and Microsoft Fabric.
• SaaS, asset management, or other complex B2B environments; experience scaling standards across onshore and offshore teams.
• Bachelor’s degree in Computer Science, Engineering, or related field, or equivalent experience; advanced degree a plus.

Success Measures

• Sustained AI coding tool adoption across the team’s core workflows.
• Step-change reduction in cycle time from requirement to production-ready pull request on target workflows, driven by agentic delivery.
• Rapid gains in test velocity, automated test quality, and regression confidence through agent-driven testing.
• Stronger PR quality, documentation completeness, and architecture decision traceability as AI engineering practices scale.
• Clear governance and auditability across AI-driven development, with transparent token and model usage and cost controls.
• Repeatable AI engineering playbooks scaled across onshore and offshore teams.

Leadership Attributes

• Emotional intelligence: reads the business, builds bridges, and partners with empathy, turning relationships into momentum.
• Player-coach: engages deeply with engineers while shaping strategy and leading change.
• Pragmatic AI adoption: changes how work is done but insists on guardrails, verification, and measurable value.
• Enterprise judgment: balances security, resiliency, maintainability, cost, and stakeholder trust.
• Adoption leadership: wins over adopters and skeptics by showing evidence and removing friction.
• Business partnership: ties engineering gains to customer outcomes, product velocity, and company growth.