Embark on a rewarding career journey at EY, where global scale, unwavering support, and an inclusive culture empower you to reach your full potential. Your unique perspective is vital in shaping EY's future and building a better world. This role focuses on the exciting intersection of Infrastructure and Artificial Intelligence, seeking an expert to design, develop, and implement AI/LLM/Agentic solutions for critical infrastructure challenges like capacity planning, incident resolution, observability, automation, cost management, and resilience.

This is a hands-on engineering and architecture position, requiring the ability to code, deploy, and integrate AI into production infrastructure workflows, moving beyond purely research-oriented tasks.

Architect and deploy AI/ML/LLM-driven solutions integrated into infrastructure operations, including AIOps, self-healing systems, predictive capacity planning, and automated root cause analysis.

Develop and build sophisticated agentic AI workflows, incorporating multi-step processes and tool utilization for infrastructure automation such as ticketing, monitoring, remediation, and provisioning.

Evaluate, fine-tune, and integrate various Large Language Models (LLMs), including both commercial (OpenAI, Anthropic, Azure OpenAI) and open-source options, into enterprise infrastructure tooling.

Construct Retrieval-Augmented Generation (RAG) pipelines, vector databases, and knowledge-grounding systems utilizing infrastructure documentation, runbooks, and CMDB data.

Produce production-ready code, primarily in Python, along with scripting in Bash/PowerShell, ensuring practical application rather than theoretical design.

Integrate AI solutions seamlessly with major cloud platforms (Azure/AWS/GCP), ITSM tools (ServiceNow), observability platforms (Datadog, Splunk, Prometheus/Grafana), and CI/CD pipelines.

Establish and maintain AI governance frameworks, focusing on data privacy, model security, hallucination control, and efficient cost/token management.

Collaborate with infrastructure leadership to pinpoint high-return AI use cases and formulate a strategic roadmap.

Mentor infrastructure engineers in AI-related competencies and serve as the lead for the internal AI Center of Excellence within the infrastructure domain.

Oversee the complete lifecycle of AI initiatives from Proof of Concept (POC) through pilot to production, incorporating robust MLOps/LLMOps practices.

Possess 16–18 years of experience in Infrastructure/Cloud engineering or architecture, with a minimum of 5–6 years dedicated to applied AI/ML/LLM work.

Demonstrate strong hands-on coding proficiency, particularly in Python, with expertise in API integration and SDK usage for platforms like OpenAI, Anthropic, LangChain, and LlamaIndex.

Exhibit a solid grasp of LLM fundamentals, encompassing prompting techniques, fine-tuning methodologies, embeddings, RAG principles, understanding context windows, and token/cost management.

Show practical experience in constructing agentic AI systems using frameworks such as LangGraph, AutoGen, CrewAI, or custom orchestration solutions, including effective tool-calling and function-calling capabilities.

Have prior experience with various vector databases, including but not limited to Pinecone, Weaviate, FAISS, and Azure AI Search.

Possess a deep background in infrastructure, covering cloud architecture, networking, virtualization, ITSM, monitoring/observability, and automation tools like Ansible and Terraform.

Bring experience in MLOps/LLMOps, covering model deployment, monitoring, versioning, and cost governance.

Exhibit strong architecture and solutioning skills, capable of conceptualizing end-to-end systems and effectively articulating design trade-offs.

Additional desirable qualifications include AWS/Azure/GCP AI or Solutions Architect certifications, exposure to enterprise AI governance and responsible AI frameworks, experience presenting AI strategy to CXO level, and prior work in Big 4/GDS/large enterprise infrastructure environments. Open-source contributions or published AI/agentic system POCs are also beneficial.

Key soft skills include the ability to translate ambiguous infrastructure pain points into actionable AI-solvable use cases and strong stakeholder management skills to bridge communication between infrastructure, data science, and business teams. Comfort with both hands-on coding/architecture and strategic planning (roadmap, governance) is essential.