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.
EY GDS – Senior Manager – AI & Data Architect- BAT
As part of our EY-GDS, we help our clients solve complex business challenges with the help of data and technology. We dive deep into data to extract the greatest value and discover opportunities in key business and functions like Banking, Insurance, Manufacturing, Healthcare, Retail, Manufacturing and Auto, Supply Chain, and Finance.
The opportunity
We’re looking for Senior Manager (AI Architects) with strong technology and data understanding having proven pre-sales & delivery capability. This is a fantastic opportunity to be part of a leading consulting firm as well as a part of growing consulting practise.
Your key responsibilities
Business Value Engineering & Consultative Selling
- Partner with C‑suite and senior business leaders to understand strategic priorities and identify AI-led transformation opportunities that deliver clear business value.
- Shape, articulate, build future architecture, and present compelling value propositions, solution narratives, and business cases for AI/GenAI/Agentic AI engagements.
- Domain experience from Consumer &Retail , Telecom / Media, Manufacturing & Industrial sectors, Oil & Gas
- Drive account teams in pursuit cycles, contributing to RFP responses, solution design, storyline decks, and client presentations.
- Lead client discovery and ideation workshops to define AI use cases, roadmap, and ROI frameworks.
- Drive consultative selling by helping clients understand where AI can meaningfully impact operations, customer experience, finance, and industry‑specific value chains.
AI Architecture, Design & Solutioning
- Architect end‑to‑end AI, Generative AI, and Agentic AI solutions across Azure (Azure AI, Azure OpenAI), AWS (SageMaker), and GCP (Vertex AI).
- Leverage leading AI platforms such as OpenAI, Azure OpenAI, Hugging Face, LangChain, vector databases (Pinecone, ChromaDB), and agent orchestration frameworks.
- Design architecture patterns for enterprise AI systems—including RAG, autonomous agent workflows, LLMOps, and scalable cloud-native deployments.
- Work with data engineering and data science teams to define data models, pipelines, and ML development workflows using Databricks, Synapse, Snowflake, Spark, Kubernetes, Docker, etc.
Delivery Oversight & Technical Leadership
- Oversee delivery of AI/GenAI projects to ensure architecture integrity, engineering quality, and alignment to business value commitments.
- Act as the technical escalation point during delivery, guiding solution teams and ensuring adherence to best practices.
- Review detailed solution designs, code, and architectural artifacts to maintain technical rigor.
- Mentor and coach architects, AI engineers, and delivery teams on solution design, coding standards, and engineering excellence.
Innovation, Prototyping & Thought Leadership
- Drive the development of POVs, accelerators, prototypes, and demos using Azure AI, OpenAI, NVIDIA AI, and related technologies.
- Create industry-specific and horizontal reference architectures, reusable assets, and solution frameworks.
- Represent the practice in client discussions, internal leadership forums, and external industry events on AI strategy and innovation.
Engineering & Build (Selective / Hands-on as Needed)
- Lead by example with hands‑on prototyping when required; write and review high-quality, testable code in Python and other languages.
- Guide teams in adopting modern practices for MLOps/LLMOps, DevOps, observability, and secure deployment.
To Qualify for the Role, You Must Have
- Extensive pre‑sales and consultative experience with the ability to diagnose business problems, frame value propositions, and connect AI capabilities to measurable business impact.
- 16+ years of professional experience, including 5–8 years architecting and deploying enterprise‑scale AI/ML, GenAI, and Agentic AI solutions on cloud or on‑prem environments.
- Experience in working & managing global stakeholders.
- Should have driven significant business outcomes as a part of their role.
- Strong RFP/RFI leadership experience, including solution shaping, writing technical sections, and conducting client-facing architecture and value engineering workshops.
- Deep expertise in designing AI solutions using microservices and cloud-native architectures, with strong understanding of Agentic AI, AI-ready data, and enterprise-scale LLM solution patterns (RAG, agents, orchestration).
- Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related technical field.
- Proven experience defining MLOps, LLMOps, and AgentOps architectures, including model lifecycle automation, observability, governance, and responsible AI.
- Hands-on experience deploying AI/ML solutions on Azure, AWS, and/or GCP, with strong understanding of platform-native services (Azure AI, Azure OpenAI, AWS Bedrock, GCP Vertex/Gemini).
- Strong experience using and orchestrating LLMs across cloud platforms—OpenAI, Azure OpenAI, Bedrock, Vertex AI, Gemini AI, vector DBs, and agent frameworks.
- Solid background in data modeling, SQL, and modern data architectures (Databricks, Snowflake, Synapse).
- Strong foundational understanding of machine learning, deep learning, NLP, and Generative AI.
- Advanced programming skills in Python (and optionally PySpark) for prototyping, validation, and reference implementations.
- Experience integrating enterprise‑grade security, identity, and authentication into AI/ML and LLM-based applications.
- Proven expertise deploying AI/ML workloads on Kubernetes, Web Apps, Databricks, or similar cloud-native platforms.
- Strong working knowledge of MLOps / LLMOps / AgentOps toolchains—Git, MLflow, feature stores, CI/CD, batch inference, real-time endpoints.
- Familiarity with Azure DevOps, GitHub Actions, Jenkins, Terraform, AWS CloudFormation, and related DevOps tooling.
- Working knowledge of Responsible AI principles, governance, model risk management, and compliance.
- Demonstrated experience delivering enterprise-grade, production-ready architectures and guiding delivery teams through implementation.
- Strong understanding of data strategy, cloud-native engineering, analytics modernization, and platform-driven transformation.
Ideally, You’ll Also Have
- Project and client management skills.
- Experience in solutioning and pre-sales engagements.
- Exposure to data mesh principles and domain-oriented data product design.
- Familiarity with data fabric architectures and multi-cloud data strategies.
- Familiarity with real-time analytics
What we look for
- People with technical experience and enthusiasm to learn new things in this fast-moving environment
What working at EY offers
At EY, we’re dedicated to helping our clients, from start–ups to Fortune 500 companies — and the work we do with them is as varied as they are.
You get to work with inspiring and meaningful projects. Our focus is education and coaching alongside practical experience to ensure your personal development. We value our employees and you will be able to control your own development with an individual progression plan. You will quickly grow into a responsible role with challenging and stimulating assignments. Moreover, you will be part of an interdisciplinary environment that emphasizes high quality and knowledge exchange. Plus, we offer:
- Support, coaching and feedback from some of the most engaging colleagues around
- Opportunities to develop new skills and progress your career
- The freedom and flexibility to handle your role in a way that’s right for you
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