Join EY and contribute to building a better working world. This role offers a significant opportunity to lead the advancement, scalability, and ongoing support for AI agentic solutions developed using Python, FastAPI, and LangGraph. As a Technical Lead, you will be instrumental in guiding engineering endeavors, identifying key areas for automation, establishing architectural standards, and shaping the technical direction for intelligent AI automation systems across the organization.

EY's Global Delivery Services (GDS) network plays a crucial role in our growth strategy, providing effective support to EY's global initiatives. Since 2002, GDS has grown to encompass over 80,000 professionals across ten global locations, delivering exceptional client service in areas like Consulting, Assurance, Tax, Strategy & Transactions, and Knowledge support. Enablement Services further enhances this by offering high-skilled, cost-effective services in Markets, BMC, Finance, Risk Management, Procurement, People Shared Services, and IT Service Delivery. Our innovation specialists collaborate with GDS, Service Lines, and Core Business Services to foster innovation through rapid prototyping and advanced technologies.

Lead the comprehensive design, enhancement, and support of backend systems and AI agentic workflows. Architect scalable solutions leveraging Python, FastAPI, LangGraph, and containerized environments. Proactively identify, prioritize, and implement automation opportunities within AI-powered applications. Mentor and guide engineering teams through design and code reviews, ensuring adherence to best practices. Define robust patterns for multi-step agent workflows, state management, and agent reliability. Drive troubleshooting and performance optimization efforts for backend services and AI agents. Collaborate closely with product, data science, and platform teams to ensure technical execution aligns with business strategy. Oversee schema design and integrations for MongoDB and PostgreSQL databases. Establish and enforce standards for CI/CD, observability, and overall production readiness of AI automation solutions.

Possess strong leadership capabilities with a proven ability to guide teams in developing complex backend and AI workflows. Demonstrate deep analytical and debugging skills across distributed and agentic systems. Exhibit a strong capacity to drive architectural decisions and ensure solutions are scalable, reliable, and performant. Maintain a proactive approach to identifying automation opportunities and enhancing existing workflows. Excellent communication and stakeholder management skills are essential.

Key qualifications include a Degree in Engineering, IT, or Computer Science. Core Python expertise is vital, along with a thorough understanding of data structures (Lists, Dictionaries, Tuples, Sets) and OOP principles. Significant experience in designing modular, maintainable, and high-performance Python applications is required, alongside expertise in profiling, debugging, dependency management, and unit testing. Hands-on experience with schema design and integration for MongoDB and PostgreSQL is also necessary.

Proficiency in FastAPI and backend architecture is crucial, evidenced by a proven track record of architecting and delivering production-grade APIs. Strong knowledge of Pydantic models, async/await patterns, authentication frameworks, and API security is expected. Expertise with Docker, Docker Compose, YAML configurations, and container networking is vital.

Experience with LangGraph and AI agentic workflows is a key requirement, including architecting multi-step agent workflows with nodes, edges, and centralized state management. The ability to define patterns for conversation state handling, context propagation, retries, and fallback logic is important. Strong skills in debugging agent flows and tracing execution paths are also necessary.