Service Delivery Center, AI & Data, Machine Learning Engineer; MLE - Senior - Tampa

Location:

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!

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.

Candidate should possess deep hands‑on expertise in designing, building, and deploying scalable machine learning systems, including advanced NLP and Generative AI (LLM) solutions. This position demands strong technical leadership, a quick learning ability, a proven track record in delivering high‑value, production‑grade AI solutions, and the capacity to mentor junior engineers.

• ML System Design & Architecture: Lead the design and architecture of robust, scalable, and high‑performance machine learning systems, ensuring seamless integration with existing platforms.

• Production ML Model Deployment: Own the end‑to‑end lifecycle of deploying and operationalizing machine learning models in production environments, ensuring efficiency, reliability, and maintainability.

• Advanced AI/ML Engineering: Develop, optimize, and implement advanced machine learning algorithms and statistical models, focusing on engineering best practices for performance and scalability.

• Generative AI & NLP System Development: Engineer and integrate cutting‑edge Generative AI (LLM) and Natural Language Processing (NLP) solutions. This includes designing efficient prompting strategies, developing LLM‑based data augmentation techniques, and implementing Retrieval‑Augmented Generation (RAG, including advanced RAG) to enhance model capabilities within production systems.

• Deep Learning Infrastructure: Design and build systems to effectively apply and deploy deep learning techniques (ANN, LSTM, CNN, BERT, XLNet, Transformers, neural & LLM‑based embeddings) for state‑of‑the‑art AI applications at scale.

• MLOps & Automation: Establish and implement MLOps practices, including CI/CD pipelines, automated testing, monitoring, and retraining strategies for ML models to ensure continuous improvement and stability.

• Performance Optimization: Optimize ML models and underlying infrastructure for computational efficiency, speed, and resource utilization.

• Technical Leadership & Mentorship: Drive technical excellence, promote best coding practices, perform code reviews, and provide mentorship to junior engineers.

• Cross‑Functional
  
  Collaboration:
  
  Partner closely with data scientists, product managers, and other engineering teams to translate complex business requirements into technical ML solutions and ensure successful delivery.

• Risk Management & Compliance: Integrate risk assessment and compliance considerations into ML system design and deployment, ensuring adherence to applicable laws, regulations, and internal policies to safeguard the firm’s reputation and assets.

• Experience:

• 8+ years of hands‑on experience in Machine Learning Engineering, MLOps,…