Global SVP - AI Engineering & Platform

Quotacom; Massachusetts; On-site; USD 260,000 - 420,000; Full time

Job summary

Quotacom in the United States is seeking a Global SVP - AI Engineering & Platform to build and lead the core AI engine for the enterprise product suite. You will own AI infrastructure, MLOps/LLMOps, and production deployment across the organization.

The role combines heavy distributed systems engineering with applied AI, guiding a world-class team of AI and platform engineers across North America and global sites, shaping governance, performance, and cost strategies.

Qualifications

• 10+ years of software/data engineering experience with 5+ years in senior leadership roles.
• Proven track record scaling AI/ML into production-grade systems.
• Strong background in developer-facing platforms and SaaS infrastructure.

Responsibilities

• Design, build, and scale a central AI platform for product teams to deploy AI/ML apps.
• Lead MLOps/LLMOps, CI/CE, feature management, and latency/cost governance.
• Partner with product leaders to translate requirements into production architectures.
• Own governance: evaluation frameworks, guardrails, security, privacy, compliance.
• Scale teams across North America and global sites; manage vendor relationships.

Skills

Distributed systems

Backend engineering

AI/ML tooling

Cloud platforms

Leadership

Education

Bachelor's or Master's in CS/SE

Tools

Kubernetes

Docker

Terraform

Databricks

MLflow

Kubeflow

LangChain

LlamaIndex

Ray

Job description

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.

At Quotacom, we take the security and privacy of your personal data very seriously, any data we hold will be in accordance with data protection legislation. Full details of our privacy notice can be found at www.quotacom.com/privacy-notice/