Responsibilities

Bridge business vision and deep technology execution, driving enterprise-grade AI solutions that are trustworthy, scalable, and aligned with organizational goals.
Strategic Advisory: Partner with CxOs and senior technology leaders to define AI transformation roadmaps, enterprise AI strategy, and operating models for responsible and sustainable AI adoption.
Ideation and Design Leadership: Facilitate design thinking sessions and co-innovation workshops with clients to identify impactful AI opportunities across business domains.
Architecture and Solution Design: Translate business and data needs into target-state AI architectures — covering model lifecycle, agentic systems design, and cloud-native AI platforms.
Infrastructure and Capacity Planning: Design AI infra blueprints spanning data, compute, GPU/TPU capacity, observability, and security, ensuring optimal cost-performance balance and resilience.
Cross-Industry Engagement: Lead solutioning across key verticals such as financial services, manufacturing, retail, telecom, and public sector, tailoring architectures to industry-specific needs.
Governance and Compliance: Establish frameworks for responsible AI, model risk management, and regulatory compliance.
Thought Leadership: Represent the firm in client advisory councils, external forums, and conferences; publish thought leadership on enterprise AI architecture and innovation practices.
Team Mentorship: Guide solution architects and engineers in designing, deploying, and optimizing modern AI systems leveraging LLMs, multi-agent frameworks, and MLOps pipelines.

Requirements

BS degree in Mechanical Engineering, Engineering Technology, or a special combination of education and experience with demonstrated accomplishments.
18+ years in technology/solutions architecture, with 3+ years in AI-led transformation roles
Proven experience as an Enterprise/Principal Architect or equivalent senior advisory role in AI, cloud, or digital transformation.
Mastery across multiple AI domains: generative AI, agentic systems, predictive analytics, and intelligent automation.
Strong understanding of AI infrastructure, including GPUs, model serving, orchestration, data pipelines, and observability tooling (e.g., Ray, Kubernetes, MLflow).
Hands-on knowledge of cloud platforms (AWS, Azure, GCP) and hybrid architectures for AI workload optimization.
Familiarity with enterprise governance, capacity planning, and FinOps considerations for large-scale AI deployments.
Excellent communication and storytelling skills to engage both executive and technical stakeholders.
Cross-industry perspective with consulting or advisory experience across at least three major verticals.
Advanced degree in Computer Science, AI/ML, or related field preferred; additional executive education in strategy or innovation is a plus