AI Platform Engineering Manager

Ernst & Young LLP ( EY India ); Bengaluru; Presencial; INR 4.200.000 - 6.600.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Leadership role

Global AI projects

Continuous learning programs

Descripción de la vacante

EY India seeks an AI Platform Engineering Manager to own deployment, scaling, and reliability of agentic AI solutions across a hybrid infra landscape. Lead a team to turn AI prototypes into enterprise-grade platforms with global reach, security, and cost efficiency.

Role sits at the intersection of platform engineering, MLOps/LLMOps, and systems architecture. You will design CI/CD and MLOps pipelines, manage hosting strategies for hosted and self-hosted models, and collaborate with AI engineers,

Formación

• 5–8 years of experience in platform engineering, infrastructure/DevOps, SRE, or MLOps roles, with 2+ years in AI/ML in production.
• Experience deploying and scaling platforms across AWS, Azure, GCP and on-prem/hybrid environments.
• Hands-on with hosting LLM APIs and self-hosted/open-source models in production.

Responsabilidades

• Own platform deployment for agentic AI solutions across multi-cloud and on-prem environments.
• Design and manage CI/CD and MLOps/LLMOps pipelines for model deployment, versioning, rollback, and evaluation at scale.
• Architect for scale and resilience to serve 100,000+ concurrent users globally with uptime, latency, and cost targets.
• Lead a team of platform/infrastructure engineers, mentor, and manage delivery timelines.
• Collaborate with AI engineers, Responsible AI, and product design to translate workflows into production systems.
• Manage model-hosting strategy including hosted APIs and self-hosted/open-source models for data residency/compliance needs.
• Build observability and monitoring — tracing, logging, cost monitoring, drift detection, dashboards.
• Drive security, compliance, and governance with InfoSec and Responsible AI teams in regulated contexts.
• Optimize infrastructure cost and performance, balancing GPU/compute across cloud and on-prem.
• Contribute to platform strategy and roadmap, advising leadership on build-vs-buy decisions.

Conocimientos

Terraform

Kubernetes

Docker

GitHub Actions

Jenkins

GitLab CI

Python

Go

Java

MLOps/LLMOps

Herramientas

Terraform

Pulumi

CloudFormation

Kubernetes

Docker

GitHub Actions

Jenkins

GitLab CI

Python

Go

Java

MLOps/LLMOps

Descripción del empleo

MS-AI4AI-PlatformEngineering-Manager At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all.

Job Title: AI Platform Engineering Manager

Role Overview

EY GDS's Managed Services “AI for AI” team is looking for an AI Platform Engineering Manager 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.
• Contribute to platform strategy and roadmap , advising leadership on build-vs-buy decisions, emerging tools, and infrastructure investments.

What We’re Looking For

• Experience 5–8 years in platform engineering, infrastructure/DevOps, SRE, or MLOps roles, with at least 2+ years working on AI/ML or LLM-based systems in production.
• Demonstrated experience deploying and scaling software/AI platforms across multiple cloud providers (AWS,Azure, GCP) and in on-prem/hybrid environments.
• Experience deploying and managing both hosted LLM APIs and self-hosted/open-source models (e.g., Llama, Mistral) in production.
• Experience building or operating agentic AI systems or complex multi-step AI workflows is highly preferred.
• Prior experience managing or leading a team of engineers.
• Technical Skills Strong proficiency in Infrastructure-as-Code (Terraform, Pulumi, or CloudFormation).
• Hands‑on experience with Kubernetes, containerization (Docker), and orchestration at scale.
• Experience with CI/CD tooling (GitHub Actions, Jenkins, GitLab CI, etc.).
• Familiarity with MLOps/LLMOps tooling.
• Understanding of model-hosting economics, GPU resource management, and inference optimization techniques (quantization, batching, caching).
• Working knowledge of enterprise security, data residency, and compliance requirements in regulated industries.
• Proficiency in at least one major programming language (Python strongly preferred; Go or Java a plus).
• Soft Skills / Leadership Proven ability to lead engineering teams in fast‑paced, ambiguous, build-from-scratch environments.
• Strong cross‑functional collaboration skills — comfortable working with Data SMEs, Product, Responsible AI, and business stakeholders.
• Clear communicator who can translate technical trade-offs into business impact for non-technical leadership.
• Comfortable operating in a "zero to one" environment — building platform foundations rather than maintaining existing ones.

What We Offer

• A leadership position within a globally recognized team at the cutting edge of AI/ML innovation, providing the opportunity to work on transformative projects with significant impact.
• Access to an extensive network of professionals and resources across the globe, fostering an environment conducive to continuous learning and career advancement.
• A diverse and inclusive culture that celebrates individuality and encourages the expression of ideas, facilitating collaborative team efforts and personal growth.
• Comprehensive support and guidance to develop into a transformative leader, equipped to navigate and excel in today's rapidly evolving technological landscape.

EY | Building a better working world

EY exists to build a better working world, helping to create long‑term value for clients, people and society and build trust in the capital markets. Enabled by data and technology, diverse EY teams in over 150 countries provide trust through assurance and help clients grow, transform and operate. Working across assurance, consulting, law, strategy, tax and transactions, EY teams ask better questions to find new answers for the complex issues facing our world today.

Experience Level Mid Level