Senior Director, AI Engineering and Delivery

JOB DESCRIPTION:

Executive Summary

The organization is making a strategic investment in AI and Generative AI and is creating a senior leadership role to architect, scale, and operationalize AI as a core platform capability.

This is a rare opportunity for a deeply technical, platform-oriented AI leader to shape how AI is engineered, governed, and consumed across a complex, regulated, multi business environment—moving the organization from pockets of innovation to enterprise-wide AI at scale.

The Head of AI Engineering and Delivery will lead the design, build, and evolution of enterprise AI and Generative AI teams and platforms for a global organization operating in life science, medical technology-driven markets. This leader will bring deep technical credibility across software engineering, data engineering, AI / machine learning, and cloud-native architecture, combined with a proven ability to build and lead technical teams operating within a highly regulated environment.

The role is responsible for creating reusable, secure, and scalable AI capabilities that empower product teams, business units, and operations to rapidly develop and deploy AI-driven solutions. The role will serve as a senior engineering and architecture authority for AI platforms, ensuring consistency, governance, and speed while enabling innovation across the enterprise.

Strategic Mandate

• Build and lead a new AI Engineering & Delivery organization operating across three layers: Platform, Delivery, and Enablement
• Establish AI and GenAI as core enterprise platforms, not bespoke solutions.
• Enable self-service AI capabilities for product, engineering, and analytics teams.
• Balance innovation velocity with regulatory compliance and operational resilience.
• Drive measurable business outcomes across customer experience, risk, operations, and productivity.
• Build and lead delivery teams to execute on the strategic mandate, developing a future focused delivery operating model.

Key Responsibilities

Define & Execute AI Platform Strategy

• Set and drive a unified, cross-business-unit AI platform strategy, ensuring seamless integration across products, services, and geographies
• Establish AI and GenAI as core enterprise platforms — not one-off solutions
• Champion API-first, platform-based architectures that accelerate time-to-market while reducing total cost of ownership
• Drive alignment across architecture proposals to maximize reuse, standardization, and leverage of shared AI and software services
• Plan and implement overall AI strategy; develop enterprise priorities and facilitate business and IT governance related to information design and business insight delivery

Build & Scale AI Engineering Delivery

• Build and lead the AI Engineering & Delivery organization spanning Platform, Delivery, and Enablement
• Establish best-in-class delivery practices for AI, Software, and Data Engineering — including discovery, build, test, automation, validation, observability, and reliability
• Own the end-to-end AI and data engineering ecosystem: cloud-native platforms, AI/ML systems, connectivity, and secure data pipelines
• Drive end-to-end observability across data pipelines, model inference, tool execution, and agent outcomes — with clear SLIs/SLOs for quality, latency, reliability, and cost
• Standardize ML and agent development workflows to reduce time-to-production and eliminate bespoke infrastructure across teams

Enable GenAI & Emerging Technology at Scale

• Partner with business unit leaders to incubate, industrialize, and scale AI and Generative AI capabilities, including:

• Machine learning and advanced analytics
• GenAI copilots, autonomous agents, and intelligent assistants
• Agent lifecycle management: CI/CD, model registries, lineage, and access control
• RAG, prompt orchestration, evaluation, and guardrails
• Process optimization and reengineering
• Modern data science platforms and development frameworks

• Make agent evaluation and experimentation default platform capabilities — offline evaluation, pre-deployment quality gates and continuous post-deployment monitoring
• Translate innovation into production-grade, governed AI systems that deliver measurable business value

Governance, Risk & Responsible AI

• Embed Responsible AI principles into platform design and engineering practices from the start
• Partner with Risk, Compliance, Legal, and Security to ensure model governance, lifecycle controls, and regulatory compliance across jurisdictions
• Ensure AI-enabled systems meet enterprise standards for security, performance, resilience, and regulatory compliance — including FDA, SOX, MoH, and regulations applicable to pharmaceutical, food, and medical device industries
• Implement and maintain compliance controls and policies applicable to pharmaceutical, food, and medical device industries
• Act as a senior voice in AI risk and governance forums across the enterprise

Organizational Leadership & Influence

• Recruit, develop, and retain world-class technical talent; foster a culture of excellence, accountability, and continuous learning
• Provide clear leadership, mentoring, and guidance to senior leaders, principal engineers, and architects across the enterprise
• Act as a connective force across Technology, Product, Operations, Cybersecurity, Compliance, and Commercial teams
• Serve as a trusted advisor to executive leadership on technology strategy, investment decisions, and transformation roadmaps
• Work in partnership with business and IT to govern total cost of investment for existing reporting environments with a focus on standardization and consolidation

Required Experience & Background

Technical & Platform Foundation

• 15+ years of experience in software engineering and large-scale platform development.
• Demonstrated success building and scaling enterprise platforms in financial services, fintech, or global technology firms.
• Strong expertise in:
• Distributed systems and modern software architecture
• Cloud platforms (AWS, Azure, GCP) in regulated environments
• API, microservices, and event-driven architectures
• Platform reliability, observability, and cost management

AI, ML & GenAI Expertise

• Proven track record delivering production AI and ML systems in real-world, regulated contexts.
• Hands-on experience with: Machine learning lifecycle management (MLOps); Model monitoring, retraining, and performance management; Generative AI and foundation models (LLMs); RAG, prompt orchestration, evaluation, and guardrails; Experience operationalizing AI with risk controls, explainability, and governance.

Leadership & Enterprise Impact

• Experience leading large, globally distributed engineering teams.
• Strong stakeholder management skills across Technology, Risk, Compliance, and Business leadership.
• Demonstrated ability to shift organizations toward platform-led, reuse-driven delivery models.
• Track record of aligning AI platform investments to revenue growth, cost efficiency, risk reduction, or customer outcomes.

Education

• Bachelor’s degree in computer science, engineering, or a related technical discipline required.
• Advanced degree (Master’s or PhD) in Computer Science, AI, Machine Learning, or Data Science preferred.

Leadership Characteristics

• Proven leader of large, global, multidisciplinary teams
• Platform mindset with a bias toward reuse, leverage, and scale
• Clear communicator who can translate complexity into executive-level decisions.
• Credible with engineers and influential with senior business and risk leaders
• Technically authoritative yet business oriented.
• Comfortable operating in highly regulated, high-stakes environments.

Success Profile (First 12–24 Months)

• A unified AI and GenAI platform is live and broadly adopted across the enterprise.
• Product and business teams can rapidly build AI capabilities using standardized services.
• AI risk, governance, and compliance are embedded by design, not retrofitted.
• AI engineering is viewed as a strategic technology capability enabling speed, safety, and scale, delivering measurable outcomes.

The base pay for this position is

In specific locations, the pay range may vary from the range posted.

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