Senior Manager of Data, AI Engineering

Syndigo•Chicago, IL

5d•Hybrid

About The Position

Syndigo powers the continual flow of data and content throughout the entire commerce ecosystem— accelerating delivery of accurate and compelling information that increases sales on every shelf. We are the recognized leader in software and services for the management of master data, product information, digital assets, and content syndication and analytics across industries including grocery, foodservice, hardlines, home improvement, oil & gas, pet, health and beauty, automotive, apparel, and healthcare products. Syndigo serves the industry’s largest two-sided network, connecting more than 50,000 global users across 12,000+ global brands with more than 1,750 global retailers. Basically, we're the people that deliver the rich, accurate product content that helps consumers shop online with confidence, and helps brands and retailers operate efficient product supply chains. We cannot do all of this without our amazing employees who make the magic happen here at Syndigo. As we continue to grow, we’re always looking to identify talented individuals to join our team. This is a hybrid position that requires 2 days per week in the office at our Chicago location We’re looking for a results-driven Senior Manager of Data, AI Engineering to build, scale, and lead high-performing teams delivering production-grade AI systems. This role sits at the intersection of engineering, data science, and product - owning the end-to-end lifecycle from concept through deployment, monitoring, and continuous optimization. You’ll play a critical role in shaping our AI strategy, translating business needs into scalable solutions, and driving execution discipline across complex, enterprise-grade systems.

Requirements

8–12+ years in software engineering, with 4–6+ years in AI/ML or data platforms
2–4+ years managing and developing engineering teams
Proven experience leading teams delivering production-grade AI/ML systems
Strong experience with: ML pipelines, model deployment, and MLOps
Strong experience with: Modern AI stack (Python, Databricks, cloud platforms, APIs, LLM/GenAI frameworks)
Strong experience with: Enterprise-scale data systems and architectures
Experience driving end-to-end delivery from concept to production and ongoing optimization
Track record of improving engineering execution and delivery predictability
Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field
Experience in enterprise SaaS, data, or AI-driven organizations
Background in high-growth or transformation environments preferred

Responsibilities

Lead and scale AI/ML engineering teams delivering production-ready systems (not just prototypes)
Drive end-to-end ownership of ML lifecycle: data pipelines, model development, deployment, observability, and iteration
Partner cross-functionally with Product, Data Science, Architecture, and Engineering to align priorities and execution
Translate business requirements into AI/ML solutions, technical roadmaps, and delivery plans
Establish and improve engineering execution standards (planning, estimation, delivery, quality)
Ensure scalability, reliability, and cost efficiency of AI/ML systems in production
Build and evolve MLOps practices, including monitoring, model health, and performance optimization
Use data and metrics to drive decision-making, prioritization, and continuous improvement
Coach, mentor, and develop engineering talent while raising the overall performance bar
Communicate complex AI concepts clearly to executive stakeholders