Join EY and contribute to an exceptional career path with global scale and support. We are building an AI Research & Model Engineering Center of Excellence (CoE) focused on next-generation AI and GenAI capabilities. This CoE aims to accelerate enterprise AI adoption and create differentiated business value by researching, engineering, and optimizing AI solutions.

This is a highly technical Individual Contributor role for engineers passionate about solving complex AI problems, building enterprise-grade AI platforms, and driving innovation from research to production. You will be instrumental in transforming innovative AI ideas into production-grade products and AI-as-a-Service offerings.

Drive technical excellence in AI Engineering, focusing on developing, evaluating, and optimizing enterprise-scale AI and Generative AI solutions. This includes designing production-grade AI applications, building reusable AI platforms, and fine-tuning LLMs and RAG architectures. You will also be involved in backend and platform engineering, designing scalable services, and integrating AI solutions with enterprise systems.

Responsibilities extend to cloud infrastructure and DevOps, utilizing Azure OpenAI and cloud-native services, deploying applications with Docker and Kubernetes, and maintaining CI/CD pipelines. You will implement enterprise controls for AI governance, security, and responsible AI practices, ensuring compliance and supporting AI observability.

Collaboration is key, working with data engineers, cloud teams, security, and business stakeholders to deliver scalable AI solutions. You will participate in architecture and code reviews, drive engineering excellence through reusable components and automation, and support production operations.

We are seeking a Senior AI Engineer with 4–10 years of hands-on experience in AI, Machine Learning, Data Science, or Generative AI, underpinned by strong Python programming skills. A solid understanding of core AI concepts like LLMs, RAG, model fine-tuning, and Agentic AI is essential.

Experience with major cloud platforms (Azure, AWS, GCP) and modern MLOps/LLMOps practices is required. Hands-on exposure to NVIDIA AI technologies such as NeMo, NIM, or RAPIDS is highly desirable. Candidates should possess strong analytical and problem-solving abilities, a research mindset, and a product-focused approach to building reusable AI capabilities.

Educational qualifications include a Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Machine Learning, or a related discipline. Excellent communication and collaboration skills are necessary to effectively work within cross-functional teams and translate complex business requirements into scalable technical solutions.