Head of AI Transformation, Engineering & SaaS Operations, MD

Who We Are Looking For

The Head of AI Transformation, Engineering & SaaS Operations reports directly to the CTO and serves as a cross-functional transformation leader responsible for accelerating AI adoption across Engineering and SaaS Operations.

This leader will drive the adoption of artificial intelligence across product development and SaaS operations to improve engineering productivity, product quality, operational resilience, customer experience, and cost efficiency.

They will define and execute the strategy for an AI-enabled Product Development Lifecycle (PDLC), embedding AI throughout software engineering, quality assurance, security remediation, incident management, and SaaS support. Working closely with Engineering, Product, SaaS Operations, Security, Finance, and HR leaders, they will establish the roadmap, operating model, governance processes, and workforce capabilities required to realize measurable business value from AI investments.

Success in this role requires a hands-on transformation leader who combines strong engineering credibility with the ability to drive organizational change. The ideal candidate will help Charles River Development evolve its software delivery and SaaS operating model by leveraging AI to increase productivity, accelerate delivery, improve quality, reduce operational effort, and enhance service reliability.

Why this role is important to us

As AI fundamentally reshapes software engineering and operational support, CRD is committed to building an AI-enabled organization that delivers better outcomes for clients while improving efficiency and scalability.

This role will:

• •Accelerate feature delivery and engineering throughput through AI-assisted development and automation.
• •Improve product quality through AI-enabled testing, validation, and quality engineering practices.
• •Reduce engineering effort associated with defects, technical debt, and vulnerability remediation.
• •Transform SaaS operations through intelligent automation, proactive monitoring, and AI-assisted support workflows.
• •Reduce operational costs while improving service quality and customer experience.
• •Establish a measurable roadmap for AI adoption across the software delivery lifecycle.
• •Enable workforce evolution through new skills, roles, and operating models.
• •Deliver transparent reporting on adoption, efficiency gains, quality improvements, and business outcomes.
• •Ensure AI investments deliver measurable return on investment.

What you will be responsible for

AI Strategy & Transformation

• •Define and execute the enterprise roadmap for AI adoption across Engineering and SaaS Operations.
• •Assess organizational AI maturity and identify gaps in processes, tooling, governance, skills, and operating models.
• •Establish adoption goals, success metrics, and transformation plans aligned to business objectives.
• •Identify emerging AI technologies and evaluate strategic opportunities for adoption.

Engineering Productivity & Software Delivery

• •Drive adoption of AI-powered software development practices across the entire product development lifecycle.
• •Apply AI across requirements generation, backlog refinement, architecture, design, development, testing, release management, support, and operations.
• •Deploy and scale coding copilots, engineering agents, and software delivery automation capabilities.
• •Improve engineering productivity through automation of repetitive development and maintenance activities.
• •Accelerate modernization initiatives, including technical debt reduction and code migration programs.
• •Apply AI-driven approaches to vulnerability management, defect resolution, and security remediation.
• •Partner with architects and engineering leaders to improve architecture design, governance, and technical decision-making through AI-enabled capabilities.

Quality Engineering Transformation

• •Establish AI-enabled quality engineering practices across all product teams.
• •Implement AI-powered test generation, regression testing, quality analysis, release validation, and defect prediction capabilities.
• •Improve automated test coverage and reduce defect escape rates.
• •Increase confidence in software releases through AI-assisted quality controls.

SaaS Operations & Support Transformation

• •Transform SaaS operations through AI-powered observability, anomaly detection, incident prediction, root cause analysis, and automated remediation.
• •Implement AI-assisted support workflows that improve case triage, knowledge retrieval, investigation, and resolution.
• •Automate Customer Health Check process after weekend maintenance periods.
• •Reduce customer-impacting incidents and accelerate issue resolution.
• •Increase the percentage of issues resolved without engineering intervention.
• •Establish enterprise knowledge management capabilities that improve engineer and support productivity.

Organizational Change & Workforce Transformation

• •Define the future operating model for AI-enabled engineering and SaaS operations.
• •Recommend and lead implementation of organizational, process, and workforce changes required to operate effectively in an AI-enabled organization.
• •Develop training, enablement, and adoption programs that accelerate AI proficiency across the organization.
• •Partner with HR and business leaders to support workforce evolution and long-term capability development.

Governance, FinOps & Business Value Realization

• •Establish governance, risk management, and responsible AI practices.
• •Manage AI-related budgets, tooling investments, token consumption, infrastructure costs, and vendor relationships.
• •Develop ROI frameworks that measure productivity, quality, operational efficiency, and cost benefits.
• •Deliver executive dashboards that provide transparent reporting on adoption, outcomes, risks, and realized value.

Success metrics

Success in this role will be measured through improvements in:

Engineering Productivity: Engineering throughput, Feature cycle time, Deployment frequency, Lead time for change, AI-assisted work percentage, AI-generated code acceptance rate, Pull request review cycle time, Developer onboarding time

Quality & Security: Defect escape rate, Automated test coverage, Vulnerability remediation cycle time, Release quality metrics, Technical debt reduction

SaaS Operations: Mean Time to Detect (MTTD), Mean Time to Resolution (MTTR), Customer-impacting incident rate, Proactive issue detection rate, Automated remediation rate, Incident recurrence rate, Support cases resolved without engineering intervention

Adoption & Workforce Enablement: AI adoption and maturity by team, Percentage of lifecycle activities supported by AI, Employee proficiency and adoption metrics

Financial Outcomes: Productivity gains realized, Engineering capacity created through automation, Capacity released or staffing demand avoided, Cost per resolved incident, AI expenditure by team and use case, Return on AI investment

What we value

These skills and leadership characteristics will help you succeed in this role:

• •Proven success leading large-scale engineering, technology, or operational transformation initiatives.
• •Strong program leadership skills with the ability to drive change across multiple organizations simultaneously.
• •Experience influencing senior technology and business leaders and building alignment across competing priorities.
• •Demonstrated ability to translate emerging technologies into measurable business outcomes.
• •Strong understanding of modern software engineering practices, including cloud-native architectures, CI/CD, DevSecOps, observability, platform engineering, and site reliability engineering.
• •Experience implementing AI-enabled software development, testing, operational support, or automation capabilities.
• •Strong commercial mindset with experience establishing business cases, KPI frameworks, and ROI models.
• •Experience leading Engineering Productivity, Developer Experience (DevEx), Platform Eng