Director, AI First Engineering

Responsibilities

AI First Engineering Strategy & Delivery

Define and execute the team’s delivery roadmap for the applications in scope, using Claude Code Enterprise and agentic development as core capabilities, and partnering with Product and the business to prioritize and evolve those 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 the AI agents, LLM-powered features, and automation built into the applications in scope.

Partner with peer engineering and architecture leaders to align on shared standards, roadmap, and reusable patterns, presenting clear options and recommendations to engineering leadership.

Stay close enough to the work to prototype, review examples, challenge assumptions, and demonstrate credible hands-on engineering judgment, using AI to the fullest in your own work and setting the standard the whole team is expected to meet.

Engineering Leadership & People Management

Directly manage a high-performing AI First engineering team, owning hiring, onboarding, performance management, and individual career development for your direct reports.

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 your Solution Architects to set direction and mentor the rest of the team. Build the team’s collective AI First capability through a structured learning path, enablement sessions, champions, office hours, and communities of practice, all grounded in real JSSI codebases.

Set direction collaboratively: frame options, integrate the team's input, then commit to clear objectives and owners and drive them to completion with high communication.

Claude Code Enablement, Standards & Governance

Drive responsible enterprise adoption of Claude Code (onboarding, usage patterns, repository context, guardrails, training, and coaching), measured by meaningful workflow usage.

Create and scale reusable engineering assets: Claude Code skills, prompt libraries, MCP and context-sharing patterns, testing patterns, API standards, architecture decision records, and secure coding standards.

Establish governance for AI First 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 & Platform Alignment

Ensure the team’s AI systems and automation integrate reliably with JSSI’s enterprise platforms (Salesforce, Dynamics 365 F&O, Microsoft Fabric, and proprietary products), coordinating with the data and platform teams that own them.

Standardize how the team builds and consumes MCP servers that expose enterprise systems as model-ready tools, in partnership with the AI Solution Architect.

Establish the observability and lifecycle-management standards that keep production automations dependable as they scale.

Influence engineering standards for API design, agent-based automated testing, code quality, deployment readiness, and architecture decision documentation.

Metrics, Stakeholders & 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 Claude Code usage (active workflows, agent-assisted PRs, skill and MCP reuse), 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, translating business needs into a prioritized pipeline and managing dependencies and risk across concurrent efforts.

Evaluate AI engineering capabilities and vendors in a fast-changing market, recommending what fits JSSI’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.

Partner closely with the Product team and business stakeholders to deliver on time, on target, and on budget, managing for stakeholder satisfaction.

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 success: production systems shipped (not prototypes) with measurable outcomes; 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 & Claude Ecosystem Proficiency (Claude Strongly Preferred)

Hands-on experience with AI coding agents and agentic workflows (Claude Code strongly preferred; GitHub Copilot, Codex, or equivalent), directing agents to ship production software.

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 aligned with Anthropic’s principles.

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, aviation, asset management, or other complex B2B environments, and 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 Claude Code 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 AI First delivery.

Rapid gains in test velocity, automated test quality, and regression confidence on critical areas through agent-driven testing.

Markedly stronger PR quality, documentation completeness, and architecture decision traceability as AI First practices scale.

Clear governance and auditability across AI First development, with transparent token and model usage and cost controls by team or use case.

Repeatable AI First 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 First: 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 JSSI’s growth.