Service Delivery Center, AI&D, AI Engineer - Manager - Tampa

At EY, we’re all in to shape your future with confidence. 

 

We’ll help you succeed in a globally connected powerhouse of diverse teams and take your career wherever you want it to go.  Join EY and help to build a better working world.

 

EY is the only professional services firm with a separate business unit (“FSO”) that is dedicated to the financial services marketplace. Our FSO teams have been at the forefront of every event that has reshaped and redefined the financial services industry. This practice also has several Service Delivery Centers that are made up of high-performing US-based resources who work closely with our experienced client-serving professionals to deliver project-based work and managed services to our US-based Financial Services clients. If you have a passion for rallying together to solve the most complex challenges in the financial services industry, come join our dynamic FSO team!

 

The Opportunity

 

Data has yet to be utilized to its fullest potential. Financial institutions are looking to build smarter and more efficient ways to operate their business, through new opportunities uncovered by their data. You’ll be solving complex problems, as well as giving deliverables to our clients in the AI and data space. With support from a highly talented team, you’ll have tremendous growth opportunities, with a focus on continuous learning and skills development to become a leader that can make significant contributions to companies.

 

Key Responsibilities:

 

This role leads the design, development, and testing of advanced AI agents and multi-agent systems, ensuring scalability, security, and observability in production-grade LLM and RAG solutions. It requires expertise in agentic frameworks, prompt engineering, evaluation pipelines, and safety testing, with hands-on experience in Google-ADK, LangChain/LangGraph, and vector databases. Candidates must have a bachelor’s or master’s degree, 10+ years of software engineering experience, and strong leadership skills in delivering AI/ML projects.

 

Skills & Attributes for Success:

 

• Exceptional command in the architecture, development, and testing of AI agents.
• Build multi-agent systems with proper state and memory management, evaluation, and concurrent orchestration.
• Design systems that allow for human oversight and feedback to improve agent performance.
• Develop explainable AI frameworks to ensure model decisions are transparent and auditable.
• Architect scalable, secure, and observable production-grade LLM systems.
• Set up observability (tracing, logging, evaluation, service level indicators) for AI agents and establish mandatory evaluation gates in CI/CD pipelines.
• Integrate LLMs into systems, apply advanced prompt engineering, and fine-tune pre-trained models.
• Design, build, and implement Retrieval-Augmented Generation (RAG) systems, including vector databases, embeddings, and knowledge graphs.
• Design test suites for non-deterministic AI systems, including golden datasets and human-in-the-loop review processes.
• Define and automate agent behavior metrics (task completion rate, tool-use success rate, execution trajectory efficiency, error recovery).
• Implement evaluation pipelines to assess output quality against pre-defined rubrics.
• Instrument and analyze agent internal thought processes for debugging and improvement.
• Test agentic solutions for adversarial and safety scenarios, including prompt injection and jailbreaking.
• Test RAG components for contextual precision, faithfulness, and quality of retrieved documents.

 

Qualifications: 

 

• Bachelor’s or master’s degree in computer science, Engineering, Mathematics, or a related field.
• 10+ years of experience in software engineering, with at least 5 years focused on AI/ML solution development.
• Proven track record of delivering AI/ML projects in production environments.
• Strong leadership and mentoring skills.

 

Technical Skills:

 

• Agentic AI Architecture: Multi-agent systems, state/memory management, explainable AI, human-in-the-loop design.
• Agentic Frameworks: Hands-on experience with Google-ADK and LangChain/LangGraph.
• MLOps/AIOps: Scalable, secure, and observable LLM systems; observability setup; CI/CD evaluation gates.
• LLMs: Integration, prompt engineering (zero-shot, few-shot, chain-of-thought, Tree-of-thought, system prompts), fine-tuning.
• RAG Systems: Vector databases (Pinecone, Weaviate, PGVector), embeddings, knowledge graphs.
• Test Strategy: Test suite design for non-deterministic AI, golden datasets, human-in-