Senior Manager, AI Platform Architecture Location:
Bolingbrook, IL ( Hybrid Tues, Wed, Thurs every other month)
Duration:
Fulltime
YOU'LL ACCOMPLISH THESE GOALS BY
Strategic Platform Leadership Translate enterprise AI strategy defined by the AI Architect Principal, Enterprise Architecture, Enterprise AI Tech Leaders (AI Engineering, DS/ML Engineering, AI Emerging Tech), AI Product Teams into actionable platform roadmaps and technical priorities. Partner with data, cloud, security, and infrastructure teams to define the end-to-end AI architecture framework, including compute, model lifecycle, and deployment strategies. Evaluate emerging technologies and recommend platform enhancements to improve model performance, scalability, and sustainability. Define and deliver AI/ML/Agentic operations strategy including tool suite, standards, and technology/capability roadmap. Establish POV on key evaluations including, but not limited to buy v. build , platform assessments, tool comparisons, cost/performance optimizations Drive the technical strategy for the platform, balancing short-term needs with long-term scalability and reliability. Design, implement, and scale cloud-based infrastructures (Google Cloud Platform and Databricks) to support internal and external applications. Oversee platform architecture decisions, ensuring that the platform is robust, efficient, and capable of supporting growing business needs.
Architecture Design and Governance:
Lead the design of core AIML platform components data pipelines, model training and inference engines, orchestration workflows, and monitoring frameworks. Design for continuous training pipelines and promote automation capabilities where possible Establish architectural best practices, patterns, and standards for AI/ML development and deployment. Oversee design reviews and ensure compliance with enterprise architecture and regulatory requirements. Design and build platform to meet AI Governance requirements (including application of controls and measurement of controls). Ensure observability requirements can be met.
ESSENTIALS FOR SUCCESS
Bachelor s degree in computer science, a related field, or applicable work experience. 10+ years of experience in software development or architecture; minimum of 3+ years in a leadership role Deep knowledge of AI/ML ecosystems Experience designing MLOps pipelines and AI Ops frameworks at enterprise scale Strong understanding of cloud-native architecture (AWS, Azure, Databricks or Google Cloud Platform), MLOps frameworks, and CI/CD principles Experience with multi-agent architecture concepts: orchestration patterns, tool/skill registry, memory and state management, and agent observability Experience setting technical standards and coordinating across distributed engineering teams Proven ability to lead cross-functional engineering teams and deliver enterprise-scale AI solutions. Comfortable navigating new, novel technology solutions and managing ambiguity to deliver in evolving domains Familiarity with security and compliance standards for platform operations. Strong analytical and problem-solving skills with a data-driven mindset. Excellent communication, project management, and stakeholder engagement skills. Comfortable with presenting up to senior leadership (VP+ level); able to present technical concepts to non-technical and executive levels
