Overview

About The Opportunity

Responsibilities

• Define and execute the Data & AI engineering strategy and roadmap—align architecture, tooling, and delivery to business outcomes and go-to-market priorities.
• Lead, hire, and scale cross-functional teams (ML engineers, data engineers, MLOps, SRE) to deliver production-grade ML and data platforms.
• Architect and operationalize end-to-end ML systems: data ingestion, feature stores, training pipelines, model registry, serving, monitoring, and automated retraining.
• Establish engineering best practices: CI/CD for ML, infrastructure as code, observability, security, cost controls, and incident management.
• Partner with Product, Customer Success, and enterprise clients to translate use-cases into measurable KPIs and production SLAs; drive prioritization and delivery cadence.
• Manage vendor relationships, procurement, budgets for cloud and third-party AI tooling; mentor senior managers and build a culture of technical excellence and ownership.
• Proven track record leading engineering teams that built and operated production ML or data platforms at scale.
• Deep technical understanding of ML lifecycle and MLOps—model training, deployment, monitoring, and drift management.
• Hands-on experience designing cloud-native architectures and optimizing cloud cost/performance.
• Strong stakeholder management—ability to translate business problems into technical roadmaps and measurable outcomes.
• Experience implementing CI/CD, observability, and security practices for data and ML pipelines.
• Demonstrated ability to recruit, mentor, and develop senior engineering managers and technical leaders.
• Experience in a consulting or services environment delivering enterprise AI solutions.
• Prior ownership of platform productization (APIs, SDKs, developer experience) for internal or external customers.
• Advanced degree in Computer Science, Machine Learning, or related field (or equivalent industry experience).

Benefits

• Hybrid work model with flexible in-office days and a high-autonomy engineering culture.
• Competitive compensation, performance-linked incentives, and opportunities for equity participation.
• High-impact role: lead strategic initiatives, influence product direction, and mentor emerging leaders in a growth-focused environment.