EY - GDS Consulting - AI Enabled Automation - AI Engineer - Staff

At EY, you’ll have the chance to build a career as unique as you are, with the global scale, support, inclusive culture and technology to become the best version of you. And we’re counting on your unique voice and perspective to help EY become even better, too. Join us and build an exceptional experience for yourself, and a better working world for all.

AI Engineer

Role Overview:

We are seeking a highly skilled and experienced AI Engineers with a minimum of 2 years of experience in Data Science and Machine Learning, preferably with experience in NLP, Generative AI, LLMs, MLOps, Optimization techniques, and AI solution Architecture. In this role, you will play a key role in the development and implementation of AI solutions, leveraging your technical expertise. The ideal candidate should have a deep understanding of AI technologies and experience in designing and implementing cutting-edge AI models and systems. Additionally, expertise in data engineering, DevOps, and MLOps practices will be valuable in this role.

Your technical responsibilities:

• Assist in the development and implementation of AI models and systems, leveraging techniques such as Large Language Models (LLMs) and generative AI.
• Design, develop, and maintain efficient, reusable, and reliable Python code
• Stay updated with the latest advancements in generative AI techniques, such as LLMs, and evaluate their potential applications in solving enterprise challenges.
• Utilize generative AI techniques, such as LLMs, Agentic Framework to develop innovative solutions for enterprise industry use cases.
• Integrate with relevant APIs and libraries, such as Azure Open AI GPT models and Hugging Face Transformers, to leverage pre-trained models and enhance generative AI capabilities.
• Utilize vector databases, such as Redis, and NoSQL databases to efficiently handle large-scale generative AI datasets and outputs.
• Implement similarity search algorithms and techniques to enable efficient and accurate retrieval of relevant information from generative AI outputs.
• Ensure compliance with data privacy, security, and ethical considerations in AI applications.
• Leverage data engineering skills to curate, clean, and preprocess large-scale datasets for generative AI applications.
• Write unit tests and conduct code reviews to ensure high-quality, bug-free software.
• Troubleshoot and debug applications to optimize performance and fix issues.
• Work with databases (SQL, NoSQL) and integrate third-party APIs.

Requirements:

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
• Minimum 2 years of experience in Python, Data Science, Machine Learning, OCR and document intelligence
• In-depth knowledge of machine learning, deep learning, and generative AI techniques.
• Proficiency in programming languages such as Python, R, and frameworks like TensorFlow or PyTorch.
• Strong understanding of NLP techniques and frameworks such as BERT, GPT, or Transformer models.
• Familiarity with computer vision techniques for image recognition, object detection, or image generation.
• Strong knowledge of Python frameworks such as Django, Flask, or FastAPI.
• Experience with RESTful API design and development.
• Experience with cloud platforms such as Azure, AWS, or GCP and deploying AI solutions in a cloud environment.
• Expertise in data engineering, including data curation, cleaning, and preprocessing.
• Excellent problem-solving and analytical skills, with the ability to translate business requirements into technical solutions.
• Strong communication and interpersonal skills, with the ability to collaborate effectively with stakeholders at various levels.
• Understanding of data privacy, security, and ethical considerations in AI applications.

Good to Have Skills:

• Understanding of agentic AI concepts and frameworks
• Proficiency in designing or interacting with agent-based AI architectures
• Apply trusted AI practices to ensure fairness, transparency, and accountability in AI models and systems.
• Utilize optimization tools and techniques, including MIP (Mixed Integer Programming).
• Implement CI/CD pipelines for streamlined model deployment and scaling processes.
• Utilize tools such as Docker, Kubernetes, and Git to build and manage AI pipelines.
• Apply infrastructure as code (IaC) principles, employing tools like Terraform or CloudFormation.
• Implement monitoring and logging tools to ensure AI model performance and reliability.

EY | Building a better working world

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