Service Delivery Center, AI & Data, Machine Learning Engineer (MLE) - Senior

EY is a global leader in assurance, tax, transaction, and advisory services. The role involves architecting and deploying production-grade AI agents and multi-agent systems to transform financial services, focusing on building autonomous systems that process vast amounts of financial data.

Responsibilities:

• Design and implement multi-agent architectures using LangChain, AutoGen, or CrewAI for complex financial workflows
• Deploy production-ready LLMs fine-tuned for financial domain expertise (loan underwriting, risk assessment, regulatory compliance)
• Create RAG (Retrieval-Augmented Generation) systems that connect AI agents to enterprise knowledge bases and real-time market data
• Implement agentic reasoning systems capable of autonomous decision-making within regulatory boundaries
• Deploy AI agents serving millions of customers with sub-second latency requirements
• Build robust MLOps pipelines for continuous model improvement and A/B testing
• Implement comprehensive monitoring and observability for autonomous systems
• Optimize inference costs while maintaining performance SLAs
• Prototype breakthrough applications: AI-powered trading assistants, autonomous compliance monitors, intelligent fraud detection agents
• Collaborate with financial domain experts to translate complex regulations into AI agent behaviors
• Contribute to EY’s AI research initiatives and patent applications
• Present solutions to C-suite executives at major financial institutions

Requirements:

• 3-5 years of Python programming with production deployment experience
• Hands-on experience with LLMs: GPT-4, Claude, Llama, or similar foundation models
• Agentic AI frameworks: LangChain, AutoGen, CrewAI, or similar multi-agent orchestration tools
• Vector databases: Pinecone, Weaviate, or Chroma for semantic search at scale
• Cloud platforms: AWS SageMaker, Azure OpenAI Service, or Google Vertex AI
• MLOps practices: Model versioning, A/B testing, drift detection, and continuous deployment
• Experience with financial services applications (trading, risk, compliance, or banking)
• Knowledge of prompt engineering and in-context learning optimization
• Familiarity with model fine-tuning techniques (LoRA, QLoRA, PEFT)
• Understanding of AI safety practices and responsible AI frameworks
• Experience with real-time streaming architectures (Kafka, Flink)
• Contributions to open-source AI projects
• Good written and verbal communication skills
• A willingness and ability to travel at 0%-25%
• Valid driver's license in the US
• Valid passport