CT_NITRO Experimental AI Engineer

EY • Alpharetta, GA

2d • $92,600 - $196,800 • Hybrid

About The Position

The Experimental AI Engineer leads the design, development, and deployment of advanced generative AI models, including large language models, to drive innovation and solve business challenges. The role oversees the end-to-end data science lifecycle, from data acquisition and preprocessing through model validation and optimization, and develops machine learning algorithms designed for performance and scalability. The engineer mentors junior data scientists and partners with cross-functional teams to turn AI-driven insights into practical, scalable products and services.

Responsibilities also include integrating AI solutions with business systems, assessing and remediating data quality, designing data models, migrating data between systems, and identifying storage requirements. The role evaluates trends such as cloud computing and distributed processing, peer-reviews models with business stakeholders, automates data science infrastructure, and recommends improvements based on developments in AI.

The position calls for extensive machine learning and data experience, generative LLM fine-tuning and prompt engineering, strong Python skills, and familiarity with relevant ML frameworks, databases, cloud platforms, and distributed computing tools. A related bachelor's, master's, or postgraduate qualification is preferred. EY describes the position as part of a team-led, leader-enabled hybrid model; external client-serving employees are expected to work together in person 40–60% of the time over an engagement, project, or year.

Requirements

• At least 8 years of experience in machine learning and large-scale data acquisition, transformation, and cleaning involving structured and unstructured data.
• Experience with generative LLM fine-tuning and prompt engineering.
• Proficiency in Python for data wrangling, visualization, and modeling; strong Python programming skills.
• Experience with machine learning and NLP algorithms, including supervised and unsupervised learning.
• Knowledge of cloud ecosystems; Azure is preferred.
• Experience with machine learning frameworks and tools such as scikit-learn, Keras, TensorFlow, MXNet, PyTorch, or MLlib.
• Experience with SQL, document databases, and vector databases.
• Capability to design efficient batch processing for high volumes of data.
• Ability to assimilate and present data using visualization and storytelling, and explain insights through a data narrative.
• Experience with Git and modern software development workflows.
• Strong team and business communication skills, including the ability to explain complex concepts to nontechnical audiences.
• Ability to thrive in a fast-paced environment and work independently.
• Excellent communication skills.
• Strong analytical and problem-solving ability; self-starter, independent thinker, curiosity, creativity, ambition, and passion.
• Interpersonal, communication, collaboration, and presentation skills; customer focus; time management; constructive mindset; commitment to continuous learning; ability to take responsibility and lead decision-making in difficult circumstances; attention to detail; integrity and honesty.

Nice To Haves

• Experience with distributed computing, GPU, cloud platforms, or Big Data tools such as GCP, AWS, Microsoft Azure, Hadoop, Spark, or Databricks.
• Knowledge of containerization platforms such as Docker.
• A bachelor's, master's, or postgraduate degree in Artificial Intelligence, Analytics, Data Science, Computer Science, Engineering, Information Technology, Social Sciences, or a related field is preferred.

Responsibilities

• Lead the design, development, and deployment of generative AI models, including large language models.
• Oversee the data science lifecycle from data acquisition and preprocessing through model validation and optimization.
• Develop and deploy machine learning algorithms optimized for performance and scalability.
• Mentor junior data scientists and promote collaboration and knowledge sharing.
• Partner with cross-functional teams to integrate AI-driven insights into products and services.
• Implement AI solutions that integrate with existing business systems.
• Review complex datasets, assess data quality, and identify data cleansing needs.
• Design and implement organizational data models.
• Migrate data between systems using multiple sources and identify and implement storage requirements.
• Analyze developments such as cloud computing and distributed processing and build industry knowledge.
• Peer-review models and consult relevant business areas to ensure models fit their intended purposes.
• Automate infrastructure for the data science team.
• Stay current with AI trends and suggest improvements to systems and workflows.

Benefits

• Medical and dental coverage.
• Pension and 401(k) plans.
• Paid time off options, including designated EY Paid Holidays, winter and summer breaks, personal/family care time, and other leaves of absence.
• Flexible vacation policy.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior