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EY GDS – AI Automation Engineer – Senior, Technology Consulting

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We’ll help you succeed in a globally connected powerhouse of diverse teams and take your career wherever you want it to go.

Join EY and help to build a better working world.

About Global Delivery Services

Global Delivery Services refers to EY's worldwide network of service delivery centers. The GDS team plays an important role in EY’s strategy by ensuring effective support to EY’s growth agenda.

Our journey started in 2002 with approximately 200 people. Today we stand at 80,000+ professionals in ten locations around the world. We operate in Argentina, China, Hungary, India, Philippines, Poland, Sri Lanka, Mexico, Spain and the United Kingdom.

Client service is focused on providing Consulting, Assurance, Tax, Strategy & Transactions, and Knowledge support to our clients around the world. The teams enable account teams worldwide to provide seamless, high-quality, value-added support, helping deliver exceptional client service.

Enablement Services provides cost-effective, high-skilled, and innovative services to support EY’s global and local enablement teams. Markets, BMC, AWS, Finance and Accounting, Risk Management, Procurement, People Shared Services, IT Service Delivery and IT Global Infrastructure services, are among the services offered by Enablement Services.

Our innovation specialists serve the GDS Client Service and Enablement Services teams, along with Service Lines, Core Business Services and Sectors. The team brings the desired environment, technologies and skilled teams together for facilitation, rapid prototyping and innovative thinking. The competencies offered include analytics, digital, user experience, mobile technology, infrastructure, Microsoft technologies and open innovation.

About the Role:

EY Global Delivery Services (GDS) is seeking a highly skilled AI Automation Engineer – Senior to design, build, and optimize enterprise-grade AI automation solutions for global clients. This role emphasizes deep hands-on engineering expertise, focusing on optimizing AI systems, improving model performance, and delivering scalable, production-ready AI-enabled automation.

You will work under architect guidance while owning key solution components, leading small workstreams, and delivering high-impact AI solutions across consulting engagements. The role requires a strong blend of software engineering, applied AI, optimization techniques, and secure development practices.

The ideal candidate demonstrates the ability to independently implement and optimize AI-driven systems, contribute to reusable accelerators, and mentor junior engineers while operating in a client-facing consulting environment.

Key Responsibilities:

• Design, develop, and optimize AI-driven automation solutions using LLMs, agent frameworks, and orchestration platforms.
• Own and deliver end-to-end implementation of solution components, including integration into enterprise systems and CI/CD pipelines.
• Optimize AI systems across dimensions such as latency, cost, accuracy, and scalability (e.g., caching, batching, prompt optimization).
• Develop and enhance Retrieval-Augmented Generation (RAG) pipelines, including chunking strategies, retrieval tuning, re-ranking, and embeddings optimization.
• Implement and optimize fine-tuning strategies, including dataset preparation, prompt tuning, and model evaluation workflows.
• Build and productionize agentic systems using frameworks such as LangGraph, AutoGen, CrewAI, Semantic Kernel, or equivalents.
• Develop automated evaluation frameworks, benchmarking pipelines, and model performance metrics.
• Contribute to AI harness frameworks, reusable accelerators, and enterprise AI development standards.
• Lead small workstreams, client discussions, demos, and proof-of-concept (POC) implementations.
• Mentor and review work of junior AI Automation Engineers, ensuring engineering quality and best practices.
• Collaborate closely with architects to translate high-level designs into scalable implementations.
• Ensure adherence to secure coding, DevSecOps, and AI security best practices.

Cyber Experience (Any One Preferred):

• Application Security:
  • Secure coding practices, SAST/DAST, DevSecOps integration, CI/CD pipeline automation
  • API security, OWASP Top 10, software supply chain security, secrets management, secure SDLC
• Offensive Security:
  • Vulnerability Assessment and Penetration Testing (VAPT), red teaming, adversarial testing
  • Exposure to AI/LLM-specific risks such as prompt injection, jailbreaks, data leakage, and model misuse

Technical Skills and Expertise:

• Programming: Strong proficiency in Python; exposure to JavaScript, TypeScript, or other languages
• AI/LLM Tools: OpenAI Codex, GitHub Copilot, Claude, Cursor, Gemini, and similar developer tools
• AI Frameworks: LangChain, LlamaIndex, LangGraph, Semantic Kernel, AutoGen, CrewAI, or equivalents
• Cloud Platforms: Azure (preferred), AWS, or GCP
• AI Platforms: Azure AI Foundry, Azure OpenAI, Hugging Face, Vertex AI, or similar
• Vector Databases: Pinecone, FAISS, Azure AI Search, Weaviate, or equivalent
• Observability & Evaluation: LangSmith, PromptLayer, Azure Monitor, or equivalent tools
• DevOps & CI/CD: GitHub Actions, Azure DevOps, Jenkins, or similar
• Prototyping Tools: Replit, notebooks, rapid experimentation frameworks
• Core Concepts: RAG optimization, prompt engineering, embeddings, model fine-tuning (LoRA/basic), evaluation frameworks, agentic workflows

Soft Skills and Attributes:

• Strong analytical and problem-solving capabilities
• Ability to lead small teams and guide junior engineers
• Effective communication and client interaction skills
• Ability to thrive in fast-paced consulting environments
• Strong ownership mindset and focus on delivery excellence

Performance Expectations / KPIs:

• Improvement in model performance metrics (accuracy, relevance, hallucination reduction)
• Optimization of inference cost and latency
• Number and impact of AI automation use cases delivered
• Contribution to reusable assets, frameworks, and accelerators
• Quality of mentoring and team capability uplift

Qualifications:

• Bachelor’s degree in computer science, Engineering, AI/ML, Cybersecurity, or related field
• 4–8 years of experience in software engineering, AI/ML, or automation engineering
• Strong hands-on experience in implementing and optimizing AI/LLM-based solutions
• Experience working in client-facing consulting environments

Relevant certifications (preferred):

• Microsoft Azure AI Engineer Associate
• AWS Machine Learning Specialty
• Certified Secure Software Lifecycle Professional (CSSLP)

What We Offer:

• Opportunity to work on advanced AI engineering and optimization use cases
• Exposure to global clients and large-scale enterprise AI transformations
• Continuous learning through innovation programs, certifications, and hands-on delivery
• Collaborative environment with strong focus on engineering excellence and growth

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.