Infrastructure & Performance Optimization: Manage and optimize GPU/accelerator infrastructure (on cloud platforms like AWS, Azure, GCP or on-prem NVIDIA clusters) to support high-performance model training and inference. Drive token efficiency, latency reduction, and cost optimization in AI pipelines (through techniques like model quantization, batching, and caching).
Client Project Delivery: Lead day-to-day project execution for AI engagements across multiple industries (e.g. finance, healthcare, manufacturing, public sector). Collaborate with client stakeholders to gather requirements and define technical solutions. Ensure on-time delivery of high-quality results that meet business objectives.
Technical Leadership & Collaboration: Provide hands-on technical leadership to a cross-functional delivery team (data engineers, ML engineers, developers). Perform code reviews, troubleshoot complex issues, and enforce best practices in software engineering, MLOps, and DevSecOps. Coordinate with data scientists, cloud architects, and industry SMEs to integrate AI solutions within broader client architectures.
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You will be expected to leverage your knowledge of best practices and tools in this area to create efficient, reliable, scalable and high-performance code.
You will collaborate with functional requirements, user experience, and microservices teams to deliver high-quality output that exceeds client expectations.
Our team works with modern technologies, including:
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Automate infrastructure provisioning, configuration management, and deployment using tools such as Terraform, Ansible, Chef, or Puppet.
Collaborate with technology teams across EY Service Lines and vendor product teams helping them implement a productized strategy, consuming existing components while also greatly enhancing velocity of products through reuse.
Work with engineering teams for designing, building & productionizing components that will grow in capability while being multi-tenant, global, scalable & highly available.
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Make the frontier-vs-open-source call deliberately, on cost, latency, control, and data sensitivity grounds — and be able to defend it
Design the cloud infrastructure underneath it all: GPU orchestration, autoscaling, cost controls, VPC/networking, IAM, observability. This is not a “hand it to DevOps” role
Fine-tune, distill, and evaluate models against real task metrics — not vibes, not leaderboards.
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