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Head of AI Platform Engineering, Execution Services
Position Overview
Our client is looking for a Head of AI Platform Engineering, Execution Services who will be responsible for defining and delivering the runtime foundation that powers AI-driven capabilities across the enterprise. This leader will oversee the design, development, and operation of the platform components responsible for executing intelligent workflows, connecting models to enterprise systems, managing agent interactions, and supporting scalable AI applications in production environments.
Working across engineering, product, architecture, security, and business teams, this role will drive platform innovation that accelerates automation, enhances customer and employee experiences, and improves operational efficiency.
Key Responsibilities
• Lead strategy, architecture, and delivery for the platform services that enable AI-powered workflow execution and agent operations.
• Build and manage high-performing engineering teams through hiring, mentoring, coaching, and career development initiatives.
• Partner with product, data, security, and architecture stakeholders to deliver scalable AI platform capabilities aligned with enterprise objectives.
• Define and execute a technology roadmap for runtime services, orchestration capabilities, model access layers, enterprise integrations, and execution frameworks.
• Ensure platform solutions are reliable, secure, observable, and optimized for performance across diverse AI workloads.
• Establish engineering best practices including architecture governance, design reviews, testing standards, automation, incident management, and operational excellence.
• Collaborate with developer experience and platform teams to simplify adoption of AI services through reusable components, APIs, SDKs, and deployment patterns.
• Drive continuous improvements in scalability, resiliency, cost efficiency, and platform maintainability.
• Promote security-first design principles, ensuring compliance, auditability, access governance, and responsible data management.
• Serve as a trusted technical advisor to senior leadership, translating complex AI and platform concepts into clear business outcomes.
Required Qualifications
• Bachelor's or Master's degree in Computer Science, Engineering, Information Technology, Business Management, or a related discipline.
• Significant experience leading software engineering organizations, including management of senior technical professionals and engineering leaders.
• Strong technical expertise in distributed systems, cloud-native platforms, AI/ML infrastructure, large-scale data ecosystems, or enterprise application platforms.
• Demonstrated success leading teams responsible for production runtime environments, orchestration services, workflow engines, inference platforms, or operational AI systems.
• Experience owning product and engineering roadmaps and delivering enterprise-scale platform capabilities.
• Deep understanding of enterprise integration patterns, APIs, data access strategies, and AI retrieval architectures.
• Knowledge of semantic search, vector-based retrieval, embeddings, context management, and secure access to enterprise information.
• Proven ability to build highly available, low-latency, and fault-tolerant services with robust monitoring, alerting, and recovery mechanisms.
• Experience working across platform engineering, security, operations, data, and application teams within complex enterprise environments.
• Strong understanding of CI/CD practices, infrastructure automation, service reliability objectives, and production support models.
• Excellent communication, stakeholder management, and organizational influence skills.
Preferred Qualifications
• Hands-on experience with AI agent platforms, intelligent automation frameworks, model serving technologies, or advanced AI execution environments.
• Expertise in AWS cloud services and modern platform architectures, including services such as Bedrock, SageMaker, Lambda, Step Functions, EKS, API Gateway, DynamoDB, S3, IAM, and CloudWatch.
• Familiarity with enterprise search solutions, vector databases, retrieval pipelines, knowledge management platforms, and context-enrichment techniques.
• Experience designing and operating multi-tenant platforms with strong governance, access controls, workload isolation, and resource management capabilities.
• Background in MLOps, LLMOps, AI operations, model deployment, evaluation frameworks, and lifecycle management processes.
• Knowledge of observability practices for AI systems, including monitoring model performance, latency, usage patterns, operational health, and cost efficiency.
• Experience optimizing large-scale runtime environments through capacity planning, performance tuning, caching strategies, workload balancing, and infrastructure optimization.
• Proven track record building reusable platform services, integration frameworks, shared tooling, and standardized deployment models.
• Experience integrating enterprise applications, workflows, data platforms, and third-party services through governed and scalable integration approaches.
• Comfortable operating in highly regulated, security-conscious environments where resiliency, compliance, and operational controls are critical success factors.
Compensation: Up to $240,000 annually
Compensation is based on a range of factors that include relevant experience, knowledge, skills, other job-related qualifications.