Global SVP - AI Engineering & Platform

Executive Summary & The Opportunity

As organizations transition from experimental AI pilots to production-scale intelligent software, the primary bottleneck has shifted from model creation to engineering execution and platform scalability.

Our client is a market leader executing an aggressive, multi-year mandate to embed AI into the fabric of its enterprise product suite and core operational workflows. They are appointing a Global SVP - AI Engineering & Platform to build and lead the core engineering organization responsible for the foundational AI platform, MLOps/LLMOps infrastructure, and production deployment of intelligence across the organization.

This role sits at the intersection of heavy distributed systems engineering and cutting-edge Applied AI. It offers a senior engineering leader the opportunity to architect the core "AI Engine" of a major enterprise from the ground up.

Key Mandate & Strategic Responsibilities

• Enterprise AI Infrastructure & Platform: Design, build, and scale a central AI platform that enables internal product and engineering teams to build, deploy, fine-tune, and monitor AI/ML and LLM applications safely and efficiently.
• Production MLOps & LLMOps: Establish industry-leading practices for model deployment, continuous integration/continuous evaluation (CI/CE), feature/prompt management, vector database infrastructure, latency optimization, and cost governance across cloud environments.
• Applied AI Engineering: Partner with product leaders to translate complex business requirements into resilient, production-ready software architectures—utilizing fine-tuned foundation models, Retrieval-Augmented Generation (RAG), dynamic agentic workflows, and predictive ML models.
• Engineering Standards & Guardrails: Own the technical governance for AI deployment, including automated evaluation frameworks, model guardrails, security protocols, hallucination mitigation, and privacy/compliance controls.
• Team Scaling & Technical Culture: Scale and lead a world-class team of AI Engineers, MLOps Engineers, Platform Engineers, and Infrastructure Specialists across North America and global sites.
• Vendor & Ecosystem Strategy: Evaluate, negotiate, and manage strategic relationships with cloud providers (AWS/Azure/GCP), foundation model vendors (OpenAI, Anthropic, open-source ecosystems), and developer toolchains.

Candidate Profile & Experience Required

Executive & Technical Leadership

• 10+ years of total software/data engineering experience, with 5+ years in dedicated engineering leadership roles (Director / Senior Director level) managing distributed platform or infrastructure teams.
• Proven track record of taking AI/ML capabilities out of the lab and scaling them into mission-critical, high-availability, high-throughput production applications.
• Strong engineering instincts with a background in building developer-facing platforms, internal tooling, or SaaS platform infrastructure.

Technical Deep Dive

• Core Engineering: Strong background in modern backend and distributed systems engineering (Python, Go, Java/Scala, C++) and modern API architecture.
• AI/ML Toolchain: Deep expertise with modern AI frameworks (PyTorch, TensorFlow), orchestration tools (LangChain, LlamaIndex, Ray), vector databases (Pinecone, Qdrant, Milvus,pgvector), and MLOps platforms (Databricks, MLflow, Kubeflow).
• Cloud & DevOps: Mastery of cloud-native infrastructure (AWS, Azure, or GCP), containerization (Kubernetes, Docker), Infrastructure as Code (Terraform), and high-efficiency GPU cluster management/optimization.

Core Competencies

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related technical discipline.
• Exceptional technical authority and communication skills—capable of gaining the respect of deep technical specialists while clearly presenting architecture and ROI to C-level leadership.

📩 Apply directly via LinkedIn with a copy of your up-to-date CV.

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