CT_NITRO Experimental AI Engineer

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.

The selected candidate

Leads the design, development, and deployment of advanced generative AI models, including large language models (LLMs), driving innovation and solving complex business challenges Oversees the end-to-end data science lifecycle, from data acquisition and preprocessing to model validation and optimization, ensuring best practices and high-quality outcomes Works on developing and deploying machine learning algorithms, ensuring models are optimized for performance and scalability Mentors and guides junior data scientists, fostering a collaborative environment and promoting knowledge sharing of cutting-edge techniques and tools Partners with cross-functional teams to integrate AI-driven insights into products and services, translating sophisticated models into practical, scalable solutions that drive measurable impact

Your key responsibilities include

Implementing AI solutions that integrate seamlessly with existing business systems to enhance functionality and user interaction. Reviewing complex data sets to establish data quality and highlighting where data cleansing is required to remediate the data Designing and implementing data models to manage data within the organization, Migrating data from one system to another using multiple sources, identifying and implementing storage requirements Analyzing the latest trends such as cloud computing and distributed processing, and their uses in business, building industry knowledge Peer reviewing models and working with relevant business areas to seek their input and ensuring they are fit for the designed purpose Automating important infrastructure for the data science team. Staying current with AI trends and suggesting improvements to existing systems and workflows.

Skills and attributes for success

8+ years’ experience in machine learning, large scale data acquisition, transformation, and cleaning, both structured and unstructured data Experience with generative LLM fine-tuning and prompt engineering. Proficiency with data wrangling, visualization, and modeling in Python Experience in ML/NLP algorithms, including supervised and unsupervised learning. Good knowledge with cloud ecosystems, Azure is preferred    Experience in machine learning frameworks and tools (e.g., sci-kit-learn, Keras, TensorFlow, MXNet, PyTorch, MLlib) Strong programming skills in Python  Experience with tools in the distributed computing, GPU, cloud platforms, and Big Data domains (e.g., GCP, AWS, MS Azure, Hadoop, Spark, Databricks) is a plus Experience with SQL, Document DBs and Vector DBs Batch Processing - Capability to design an efficient way of processing high volumes of data where a group of transactions is collected over a period Data  Visualization and storytelling - Capability to assimilate and present data, as well as more advanced visualization techniques. Capability to engage and explain data creating a narrative around data visualization​ Knowledge of containerization platforms like Docker is a plus. Experience with Git and modern software development workflow. Non-Technical Skills:

Strong team and business communication skills – can walk through and approach with technical clients or explain tricky concepts to non-technical people Must thrive in a fast-paced environment and be able to work independently.  Excellent in communication skills

Bachelor/Master/PG in Artificial Intelligence, Analytics, Data Science, Computer Science, Engineering, Information Technology, Social Sciences or related fields preferred

What we look for

Strong analytical skills and problem-solving ability A self-starter, independent-thinker, curious and creative person with