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

EY • San Antonio, TX

1d

About The Position

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.

Senior Machine Learning Engineer

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.

Requirements

• 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.
• 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.
• Demonstrated ability to design scalable, fault-tolerant, and performant ML systems.
• Exceptional analytical, interpretive, and problem-solving skills with a focus on engineering challenges and innovative solutions.
• Excellent interpersonal, verbal, and written communication skills, with the ability to articulate complex technical concepts to both technical and non-technical audiences.
• Proven ability to work independently, drive projects to completion, and provide technical leadership and mentorship.

Nice To Haves

• Experience with graph neural networks, graph databases, or distributed systems for ML.

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.

Benefits

• We offer a comprehensive compensation and benefits package where you’ll be rewarded based on your performance and recognized for the value you bring to the business.
• 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.
• In addition, our Total Rewards package includes medical and dental coverage, pension and 401(k) plans, and a wide range of paid time off options.
• Under our flexible vacation policy, you’ll decide how much vacation time you need based on your own personal circumstances. You’ll also be granted time off for designated EY Paid Holidays, Winter/Summer breaks, Personal/Family Care, and other leaves of absence when needed to support your physical, financial, and emotional well-being.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed
• Number of Employees: 5,001-10,000 employees