Senior Manager AI Platform Architecture
Position Overview
The Senior Manager, AI Platform Architecture is a hands-on leader responsible for the architecture and evolution of our core AI platforms. In this role, will manage a team of skilled engineers and architects, collaborate with cross-functional stakeholders, and help set the strategic direction of platform architecture to ensure scalability, performance, and reliability across our systems.
Responsibilities
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 (GCP 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.
Cross-Functional Collaboration: Work closely with product, data science, engineering, and security teams to operationalize AI / ML capabilities across the enterprise.
Partner with the AI Architect Principal and AI Solution Architects to align agentic platform evolution with organizational priorities and technology roadmaps.
Collaborate with security and operations teams to ensure the platform is secure, compliant, and maintains high uptime and reliability.
Engage with leadership to align technical initiatives with organizational objectives.
Team Leadership and Talent Development: Manage and mentor a team of AI engineers, AI architects, and AI Ops engineers. Foster a high-performance culture emphasizing innovation, collaboration, and agile delivery.
Support career development, technical upskilling, and diversity in AI technology roles, specifically those aligned into the Architecture space.
Mentor and guide junior team members. Conduct regular performance reviews, provide career development guidance, and support team members' growth and skill development.
Own hiring, onboarding, and team-building activities to ensure the team has the right talent and skills.
Provide hands-on technical leadership, leading by example in designing and building robust, scalable systems.
Drive high standards of code quality, testing, and engineering practices.
Advocate for platform engineering best practices, ensuring systems are maintainable, extensible, and documented.
Operational Excellence: Oversee platform scalability, reliability, and cost optimization across cloud and on-prem environments.
Implement observability and monitoring tools to proactively identify performance or security issues.
Ensure platform compliance with responsible AI, data privacy, and ethical ML principles.
Build and develop architecture audit process, and execute.
Identify opportunities for automation, process improvements, and tooling that enhance platform reliability and efficiency.
Stay current with emerging technologies and industry best practices and incorporate relevant trends into the platform strategy.
Lead incident response and post-mortem reviews to continuously improve platform resilience.
Platform Performance & Reliability: Define, implement, and monitor key platform performance metrics, including system uptime, latency, and resource utilization.
Ensure the platform is scalable and cost-efficient, optimizing for performance and operational cost management.
Lead efforts to identify, troubleshoot, and resolve platform performance issues or outages.
Qualifications
- 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 GCP), 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, Client 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.