Director, Engineering – AI Platform (Agentic AI)

What You’ll Be Doing

• Drive end-to-end transformation of the SDLC, ensuring AI is embedded across requirements, development, testing, deployment, and post-release observability—not just code generation.
• Design and implement secure, compliant AI platforms with embedded governance, guardrails, and auditability.
• Establish and scale AI-powered capabilities including code assistants, agentic workflows, test automation, and developer productivity tools.
• Build and integrate a developer productivity ecosystem spanning collaboration tools, workflows, knowledge systems, and AI platforms.
• Define and track outcome-based metrics for developer productivity, software quality, and operational effectiveness, leveraging telemetry, observability frameworks, and reporting to measure AI impact at scale.
• Ensure scalability, reliability, resiliency, and cost optimization of AI and distributed systems.
• Evolve engineering operating models, delivery practices, and standards to support AI-enabled development.
• Partner with cross-functional stakeholders (Security, Risk, Legal, Infrastructure, Product) to drive safe and compliant AI adoption.
• Advise executive leadership on emerging AI trends, risks, and enterprise opportunities.
• Lead vendor evaluation, selection, negotiations, and ongoing management for AI platforms and tools.
• Lead developer adoption, training, and governance frameworks to ensure responsible, effective use of AI across engineering teams.
• Drive continuous improvement in engineering processes, quality standards, and platform capabilities.
• Define and optimize model usage strategies across use cases, balancing performance, cost, and scalability (e.g., token consumption, model selection, and workload segmentation).
• Evaluate and select AI tools, models, and platforms in a rapidly evolving landscape, aligning solutions to use case, cost, and performance requirements.

What You Bring to the Table

• Proven experience implementing AI-enabled software development lifecycle transformation in production environments, including end-to-end integration and scaling across multiple engineering teams.
• Strategic thinking with the ability to translate vision into execution.
• Strong leadership and team-building capabilities.
• Influencing and stakeholder management skills across all organizational levels.
• Advanced problem-solving and critical thinking abilities.
• Adaptability in a fast-changing, emerging technology landscape.
• Results orientation with a focus on measurable outcomes.
• Strong communication and storytelling skills for executive audiences.
• Collaborative mindset with a focus on cross-functional partnership.

Basic Qualifications

• Bachelor’s degree in Computer Science, Engineering, Data Science, Information Systems, or related field or equivalent work experience.
• 10+ years designing and delivering distributed systems in production environments.
• 5+ years leading managers and multi-level engineering teams.
• 2+ years building and scaling AI/ML or agentic systems, including multi-agent workflows and model lifecycle management.
• Experience with at least one enterprise AI platform (e.g., Azure AI, AWS Bedrock, Google Gemini, Databricks).
• Experience implementing agentic AI capabilities (e.g., orchestration, tool use, memory, evaluation).
• Experience building scalable, reliable, and cost-efficient distributed systems.
• Proficiency in one or more programming languages (Java, Python, TypeScript, or similar).
• Demonstrated experience leading engineering teams, including managing managers and developing talent.
• Strong cross-functional leadership and stakeholder influence skills.
• Hands-on experience designing and deploying AI-enabled engineering platforms—not solely defining strategy.
• Ability to translate complex technical concepts into executive-level insights.

Preferred Qualifications

• Master’s degree in Computer Science, AI, Data Science, or related field.
• Experience with RAG, GraphRAG, vector databases, and enterprise knowledge integration patterns.
• Experience with AI orchestration frameworks (e.g., LangChain, LangGraph, LlamaIndex).
• Experience implementing AI governance, responsible AI, and model risk management frameworks.
• Experience building secure AI platforms (e.g., access controls, audit logging, secrets management).
• Experience defining and tracking engineering productivity or quality metrics tied to business outcomes.
• Experience leading enterprise-scale technology or platform transformations.
• Experience managing vendor selection, negotiations, and partnerships.