AI Platform Engineering Consultant

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Job Title: AI Platform Engineering Senior Consultant

Role Overview:

EY GDS's Managed Services “AI for AI” team is looking for an AI Platform Engineering Senior Consultant to own the deployment, scaling, and reliability of our agentic AI solutions across a complex, hybrid infrastructure landscape — multi-cloud, on-prem, and hosted model environments. You'll lead a team of engineers responsible for turning AI prototypes into resilient, enterprise-grade platforms that run globally, securely, and cost-effectively.

This role sits at the intersection of platform engineering, MLOps/LLMOps, and agentic systems architecture.

Key Responsibilities:

• Own platform deployment for agentic AI solutions across AWS, Azure, and/or GCP, as well as on-prem and hybrid-hosted model environments (open-source and proprietary LLMs).
• Design and manage CI/CD and MLOps/LLMOps pipelines for model deployment, versioning, rollback, and continuous evaluation at scale.
• Architect for scale and resilience — ensuring platforms reliably serve 100,000+ concurrent users globally with strong uptime, latency, and cost-efficiency targets.
• Lead a team of platform/infrastructure engineers, setting technical direction, mentoring, and managing delivery timelines.
• Partner cross-functionally with AI Engineers, Forward Deployed Engineers, Responsible AI leads, and Product Design to translate agentic workflows into production-ready systems.
• Manage model-hosting strategy, including trade-offs between hosted APIs (e.g., Anthropic, OpenAI, Azure OpenAI), self-hosted open-source models, and on-prem deployments for data residency/compliance needs.
• Build observability and monitoring for agentic systems — tracing, logging, cost monitoring, drift detection, and performance dashboards.
• Drive security, compliance, and governance of AI infrastructure in collaboration with Responsible AI and InfoSec teams, particularly given enterprise/regulated-client contexts (tax, cybersecurity, supply chain data).
• Optimize infrastructure cost and performance, balancing GPU/compute resource allocation across cloud and on-prem environments.