AI Director, Development Engineering

Role Description

You will lead development of current and future capabilities while shaping engineering strategy, delivery standards, architecture direction, and team performance. You will act as a change agent for modern software delivery—using AI, automation, DevSecOps, CI/CD, cloud practices, quality gates, and engineering intelligence to improve how teams build and ship.

You bring strategic judgment and practical execution. We trust you to shape the future—and make it real.

Key Outcomes

·         Define and execute engineering strategy aligned to business and product goals.

·         Improve SDLC maturity, cloud delivery, architecture governance, and quality practices.

·         Lead and develop high-performing teams in a fast-paced environment.

·         Use AI-enabled insights to improve delivery predictability, technical decision-making, and operational effectiveness.

·         Drive modernization across engineering practices, tools, and operating models.

Roles & Responsibilities

·         Provide strategic leadership to product development teams across the SDLC.

·         Establish and implement best practices and standards for engineering, QA, DevOps, secure delivery, automation, and architecture governance.

·         Work with executive and senior management and interdisciplinary teams to develop vision and strategy.

·         Oversee analysis, design, programming, debugging, interoperability, and application modification activities.

·         Identify opportunities to continuously improve SDLC practices, cloud delivery, CI/CD, quality, and engineering operations.

·         Guide prioritization and trade-offs across roadmap candidates, resourcing, timing, technical debt, and business outcomes.

·         Use approved AI tools to support architecture review, delivery-risk analysis, technical debt insights, quality reporting, documentation, and portfolio decision support.

·         Lead, mentor, and develop high-performing individuals and teams.

Education

Bachelor’s degree in Computer Science or a related field required. Master’s degree preferred.

Experience(years including)

Ten or more years of software development experience, including complex whole lifecycle software development management. Five or more years leading staff. Five or more years in software product development or management. Experience leading technology direction preferred.

Skills

Engineering strategy, SDLC transformation, agile leadership, cloud application development, CI/CD, architecture governance, quality practices, DevSecOps, technical debt management, roadmap trade-offs, stakeholder influence, change leadership, and team development.

Abilities

Set vision, translate strategy into execution, make complex trade-offs, lead through change, influence executives and teams, develop leaders, evaluate technology direction, and govern responsible AI-enabled engineering practices.

Tools & Technologies

Jira, VersionOne, Azure DevOps, TFS, MS Office, CI/CD platforms, cloud platforms, infrastructure as code tools, quality analytics, test automation frameworks, observability platforms, documentation systems, engineering intelligence tools, and approved AI-enabled engineering and leadership tools.

AI Usage and Accountability Requirements

Use approved AI tools to support strategy development, architecture analysis, technical debt review, delivery-risk analysis, quality insights, documentation, stakeholder communication, and portfolio trade-off analysis. Validate AI-generated outputs before use. Do not enter sensitive, restricted, customer, healthcare, source code, financial, regulatory, or production data into unapproved tools. Human ownership remains required for strategy, architecture direction, people leadership, budget influence, risk acceptance, stakeholder alignment, and delivery outcomes.

Success Measures / Accountable Outcomes

Engineering strategy execution, SDLC maturity, product delivery quality, cloud and CI/CD adoption, technical debt reduction, team performance, roadmap trade-off quality, stakeholder trust, responsible AI adoption, and measurable improvement in product and engineering outcomes.

AI Competency Alignment & Proficiency Expectations

Authority and Autonomy

Establishes operational objectives, policies, procedures, and work plans and delegates assignments. Develops, modifies, and executes policies that affect immediate operations and may have company-wide effect. Accountable for department budget and business impact.