EY - GDS Consulting - AIA - Gen AI NVIDIA - Senior(Big4jobs)

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. 

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• Career Family:AIA – GenAI ENGINEER with exposure on NVIDIA framework
• Role Rank: Senior
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• The opportunity

We are establishing an AI Research & Model Engineering Center of Excellence (CoE) to build next-generation AI / GenAI capabilities that accelerate enterprise AI adoption and create differentiated business value.

The CoE will focus on researching, engineering, validating, optimizing, and commercializing AI solutions that can be reused across multiple business domains and clients. Our vision is to transform innovative AI ideas into production-grade products, reusable accelerators, and AI-as-a-Service offerings.

This is a highly technical Individual Contributor (IC) role for engineers who are passionate about solving complex AI problems, building enterprise-grade AI platforms, and driving innovation from research to production.

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• Your key responsibilities
• Technical Excellence:

• AI Engineering & Agentic AI Development
• Design, develop, evaluate, validate, benchmark, and optimize AI and Generative AI solutions for enterprise-scale applications.
• Evaluate and compare foundation models, open-source LLMs, commercial models, SLMs, multimodal models, and Agentic AI frameworks to identify the best-fit solution for different business scenarios.
• Define model selection criteria by considering factors such as accuracy, reasoning capability, latency, inference cost, scalability, security, explainability, governance, deployment complexity, and total cost of ownership.
• Build repeatable evaluation methodologies and benchmarking frameworks to measure model quality, business performance, and production readiness.
• Design and implement production-grade AI applications by taking solutions through the complete lifecycle—from research and experimentation to Proof of Concept (PoC), pilot, production deployment, and continuous optimization.
• Develop and fine-tune LLMs, RAG architectures, AI agents, and domain-specific AI models to improve performance, reliability, and business outcomes.
• Build reusable AI platforms, SDKs, APIs, accelerators, and AI-as-a-Service capabilities that can be leveraged across multiple projects and clients.
• Engineer AI solutions with a product mindset, ensuring they are reusable, scalable, maintainable, and suitable for commercialization and monetization.
• Optimize AI workloads for cloud and GPU environments to improve inference performance, resource utilization, scalability, and operational efficiency.
• Research emerging AI technologies and rapidly build prototypes to evaluate their technical feasibility and business impact.
• Collaborate with architects, product managers, engineers, and business stakeholders to deliver innovative AI solutions that solve real-world business challenges.
• Contribute to enterprise AI standards, reference architectures, engineering best practices, governance frameworks, and reusable design patterns.

• Backend & Platform Engineering
• Design and build scalable backend services using Python, FastAPI, REST APIs, microservices, and event-driven architectures.
• Develop reusable AI platform components, services, APIs, and integrations to accelerate enterprise AI adoption.
• Integrate AI solutions with enterprise systems, third-party applications, workflow platforms, and data services.
• Troubleshoot and optimize AI pipelines, APIs, vector stores, backend services, and cloud-native applications.
• Implement scalable deployment strategies using containerized and cloud-native architectures.

• Cloud, Infrastructure & DevOps
• Develop enterprise AI solutions using Azure OpenAI, Azure AI Services, and cloud-native services.
• Deploy and manage applications using Docker, Kubernetes, OpenShift, and container orchestration platforms.
• Build and maintain CI/CD pipelines using GitHub Actions, GitLab CI, and modern DevOps tooling.
• Ensure production readiness through monitoring, observability, automated testing, release management, and operational excellence.
• Support deployment and lifecycle management across development, testing, staging, and production environments.

• AI Governance, Security & Responsible AI
• Implement enterprise controls for PII protection, data privacy, AI security, compliance, and responsible AI practices.
• Support AI governance initiatives through monitoring, auditability, access controls, and compliance frameworks.
• Contribute to AI observability practices, including monitoring model behavior, hallucination risks, accuracy, latency, and retrieval quality.
• Ensure adherence to enterprise architecture, security standards, and engineering best practices.

• Team Collaboration & Delivery Excellence
• Collaborate with data engineers, cloud and platform teams, security teams, product owners, and business stakeholders to refine requirements and deliver scalable AI solutions.
• Participate in architecture reviews, code reviews, testing reviews, and technical design discussions.
• Drive engineering excellence through reusable components, documentation, automation, and quality standards.
• Support production operations including troubleshooting, performance tuning, root-cause analysis, and continuous improvement.

Skills and Attributes:

• Educational Background
• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Machine Learning, or a related discipline.
• Candidates from Tier-1 engineering institutions are preferred.

• Professional Experience
• 4–10 years of hands-on experience in Artificial Intelligence, Machine Learning, Data Science, or Generative AI with strong Python programming skills.
• Solid understanding of Machine Learning, Deep Learning, Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), model fine-tuning, and Agentic AI.
• Experience working with Azure, AWS, or GCP and modern AI engineering, deployment, and MLOps/LLMOps practices.
• Hands-on exposure to NVIDIA AI technologies such as NeMo, NIM, RAPIDS, GPU computing, inference optimization, or similar AI infrastructure is highly desirable.
• Strong analytical thinking, problem-solving ability, debugging skills, and a research mindset with a passion for continuous learning.
• Product mindset with the ability to build reusable, production-grade AI capabilities rather than one-off project solutions.
• Excellent communication and collaboration skills with the ability to work effectively in cross-functional teams.

• Soft Skills
• Strong analytical and problem-solving capabilities.
• Excellent communication and stakeholder management skills.
• Ability to translate complex business requirements into scalable technical solutions.
• Strong collaboration skills across engineering, product, and business functions.
• Commitment to engineering excellence, continuous learning, and innovation

Preferred Certifications

• NVIDIA GenAI and LLM focused, AI/ML

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• What Success Looks Like
• In this role, you will:

• Build AI solutions that move seamlessly from prototype to enterprise-scale production.
• Develop evaluation and benchmarking frameworks that establish trust in AI systems.
• Enable the organization to select the right AI model for every business problem through structured evaluation and evidence-based decision making.
• Create reusable AI platforms, accelerators, and engineering assets that improve delivery speed and quality.
• Develop commercial-grade AI capabilities that can be productized, monetized, and adopted across multiple clients and business functions.

Why Join Us

• Be at the forefront of AI-driven innovation across multiple client sectors.
• Work with global clients to drive real business impact.
• Collaborate with a team of AI experts, analytics leaders, and industry specialists in a highly entrepreneurial environment.

What we offer

EY Global Delivery Services (GDS) is a dynamic and truly global delivery network. We work across six locations – Argentina, China, India, the Philippines, Poland and the UK – and with teams from all EY service lines, geographies and sectors, playing a vital role in the delivery of the EY growth strategy. From accountants to coders to advisory consultants, we offer a wide variety of fulfilling career opportunities that span all business disciplines. In GDS, you will collaborate with EY teams on exciting projects and work with well-known brands from across the globe. We’ll introduce you to an ever-expanding ecosystem of people, learning, skills and insights that will stay with you throughout your career.

• Continuous learning: You’ll develop the mindset and skills to navigate whatever comes next.
• Success as defined by you: We’ll provide the tools and flexibility, so you can make a meaningful impact, your way.
• Transformative leadership: We’ll give you the insights, coaching and confidence to be the leader the world needs.
• Diverse and inclusive culture: You’ll be embraced for who you are and empowered to use your voice to help others find theirs.

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• EY | Building a better working world 
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• 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.  
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• Enabled by data and technology, diverse EY teams in over 150 countries provide trust through assurance and help clients grow, transform and operate.  
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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.