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Jobs · Engineering · Florida

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

EY · Tampa, FL · 3 wk ago
On-siteEngineering$66k–$134k/yrFull-time

About the role

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.

Responsibilities

  • 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.

Qualifications

  • Experience:
    • 8+ years of hands-on experience in Machine Learning Engineering, MLOps, or AI system development.
    • Minimum of 2 years of direct experience in engineering and deploying Generative AI/LLM solutions in production.
  • Technical Skills:
    • Deep proficiency in Python for production-grade ML development, with expertise in relevant libraries (scikit-learn, pandas, SpaCy, TensorFlow, PyTorch, Hugging Face Transformers).
    • Strong experience with PySpark for large-scale data processing and building robust data pipelines.
    • Proficiency in big data frameworks (Hadoop, Spark, Hive, Hue) and experience with streaming technologies.
    • Extensive experience with MLOps tools and practices (e.g., Docker, Kubernetes, MLflow, Airflow, CI/CD for ML).
    • Proven experience in designing, implementing, and deploying NLP and deep learning models to production.
    • Hands-on experience with Generative AI development, including engineering prompting strategies, RAG implementation, and LLM fine-tuning and integration (e.g., Langchain, LlamaIndex).
    • Familiarity with cloud platforms (AWS, Azure, GCP) and their ML services.
    • Good to have: Experience with graph neural networks, graph databases, or distributed systems for ML.
  • System Design & Architecture: Demonstrated ability to design scalable, fault-tolerant, and performant ML systems.
  • Problem-Solving: Exceptional analytical, interpretive, and problem-solving skills with a focus on engineering challenges and innovative solutions.
  • Communication: Excellent interpersonal, verbal, and written communication skills, with the ability to articulate complex technical concepts to both technical and non-technical audiences.
  • Autonomy & Leadership: Proven ability to work independently, drive projects to completion, and provide technical leadership and mentorship.

Pay

The base salary range for this job in all geographic locations in the US is $65,500 to $134,000. The base salary range for New York City Metro Area, Washington State and California (excluding Sacramento) is $78,600 to $152,100. Individual salaries within those ranges are determined through a wide variety of factors including but not limited to education, experience, knowledge, skills and geography.

Schedule

Our expectation is for most people in external, client serving roles to work together in person 40-60% of the time over the course of an engagement, project or year.

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