EY - GDS Consulting - AIA - Gen AI - Senior Manager

The opportunity

We are the only professional services organization who has a separate business dedicated exclusively to the financial services marketplace. Join Digital Engineering Team and you will work with multi-disciplinary teams from around the world to deliver a global perspective. Aligned to key industry groups including Asset management, Banking and Capital Markets, Insurance and Private Equity, Health, Government, Power and Utilities, we provide integrated advisory, assurance, tax, and transaction services. Through diverse experiences, world-class learning and individually tailored coaching you will experience ongoing professional development. That’s how we develop outstanding leaders who team to deliver on our promises to all of our stakeholders, and in so doing, play a critical role in building a better working world for our people, for our clients and for our communities. Sound interesting? Well, this is just the beginning. Because whenever you join, however long you stay, the exceptional EY experience lasts a lifetime.

We are looking for a Senior Manager – GenAI & Agentic AI to join our AI Enabled Automation practice, with 14+ years of strong hands-on experience in designing and delivering enterprise-grade AI solutions. The role requires deep technical proficiency in Python, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Agentic AI frameworks, and cloud platforms (Azure, AWS, GCP), combined with the ability to lead teams, drive innovation, and translate complex AI capabilities into measurable business outcomes. In this role, you will contribute in two key capacities:

• by leading the design, development, and deployment of GenAI and Agentic AI solutions — including LLM-based applications, multi-agent systems, and RAG pipelines — aligned to target-state architecture and client objectives, and
• by driving AI strategy, proof-of-concept initiatives, and production-grade implementations across industry verticals, leveraging cloud-native services, MLOps practices, and emerging AI technologies to deliver scalable, observable, and high-impact automation solutions..

EY’s Artificial Intelligence & Automation (AIA) team is a specialized, intelligence driven business unit that combines advanced AI, automation engineering, and deep domain knowledge to deliver transformative enterprise solutions. The AIA practice partners with clients to analyze, design, and operationalize AI led automation strategies that accelerate digital transformation and unlock measurable business value. We address organizations’ most critical challenges across process optimization, intelligent workflows, data driven decisioning, Generative AI adoption, and automation-enabled innovation. With a unique ability to translate AI strategy into actionable architectures, scalable platforms, and production grade automations, EY’s AIA team helps clients build sustainable competitive advantage, enhance productivity, and navigate disruption by embedding intelligence at the core of their business and technology ecosystems.

Your key responsibilities

• AI Strategy & Solution Design
• Lead the development and implementation of AI enabled automation solutions, ensuring alignment with business objectives and client requirements.
• Design and deploy Proof of Concepts (POCs) and Points of View (POVs) across various industry verticals, demonstrating the business value of GenAI and Agentic AI applications.
• Challenge the status quo by recommending modern AI architectures, agentic patterns, retrieval strategies, and cloud-native designs that drive innovation.
• Collaborate with cross-functional teams including architects, data engineers, and product owners to translate business requirements into scalable AI solutions.

• GenAI & Agentic AI Development
• Architect and build production-grade applications using Large Language Models (GPT, Claude, LLaMA, Gemini), including prompt engineering, fine-tuning, and evaluation.
• Design and implement Retrieval-Augmented Generation (RAG) pipelines using Vector Databases, Knowledge Graphs, and hybrid retrieval strategies.
• Build multi-agent orchestration systems using frameworks such as LangGraph, AutoGen, CrewAI, or LlamaIndex, with memory management and tool integrations.
• Develop robust API layers, integration services, and data pipelines to connect AI capabilities with enterprise systems.
• Apply strong analytical thinking, structured problem-solving, and clear technical communication in solution design.

• MLOps, Cloud & Observability
• Implement MLOps practices and tools — CI/CD for ML, containerization (Docker, Kubernetes), model versioning, and reproducibility — to ensure reliable AI deployments.
• Deploy and manage AI workloads on cloud platforms (Azure, AWS, GCP), leveraging services such as Azure OpenAI, Azure AI Search, SageMaker, or Vertex AI.
• Build observability into AI systems — monitoring latency, model drift, token usage, performance metrics, and guardrails for responsible AI.
• Ensure seamless integration of optimized solutions into the overall product or system architecture.

• Team Leadership & Collaboration
• Mentor and guide team members on AI/ML best practices, emerging GenAI technologies, and engineering standards through design discussions, code reviews, and knowledge sharing.
• Keep the team updated on the latest advancements in GenAI, Agentic AI, and related technologies to bring innovative solutions to engagements.
• Support all phases of delivery including sprints, testing cycles, deployments, and hypercare, ensuring timely delivery under tight timelines.
• Present solutions, architecture decisions, and results to both technical and business stakeholders with strong written and verbal communication.

Required Skills

• 14+ years of relevant professional experience with a strong foundation in AI/ML, with at least 2–3 years focused on GenAI and LLM-based solution development.
• Expertise in:
• Core AI/ML: Python, PyTorch, TensorFlow, Hugging Face Transformers, ML algorithms, feature engineering, model evaluation
• Deep Learning & NLP: Neural Networks, RNNs, CNNs, LSTMs, Transformer architectures (BERT, GPT), NLP pipelines
• GenAI & LLMs: GPT, Claude, LLaMA, Gemini — prompting strategies, fine-tuning (LoRA, QLoRA, PEFT), evaluation frameworks (RAGAS, DeepEval)
• Agentic AI: LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI — multi-agent orchestration, memory, planning, and tool use
• RAG & Knowledge Systems: Vector Databases (Pinecone, Weaviate, Qdrant, Azure AI Search), Knowledge Graph RAG, hybrid retrieval, chunking strategies
• Cloud & DevOps: Azure (OpenAI, AI Search, Functions, App Services), AWS, GCP — scalable model deployment, CI/CD pipelines, containerization
• Data & Integration: Data pipelines, REST APIs, distributed systems, SQL/NoSQL databases, graph databases (Neo4j)
• Strong understanding of responsible AI — bias detection, guardrails, content safety, and ethical AI practices.
• Excellent communication, logical re