Join EY GDS Consulting as a Senior Manager, focusing on AI Engineering within our AIA team. This role offers a unique opportunity to shape the future of AI and automation. You will be instrumental in building a career at the forefront of technological innovation, leveraging EY's global scale and inclusive culture to become your best self.

We are seeking a passionate AI Architect with over 15 years of professional experience, specializing in Agentic AI and automation. Your deep understanding of deploying Agentic AI systems in production, coupled with expertise in data processing, algorithms, and machine learning, will be key to developing and implementing cutting-edge AI capabilities and solutions.

As an AI Architect, you will design and implement Agentic AI/Gen AI applications across major cloud platforms (Azure, AWS, GCP). Your role involves collaborating with clients and stakeholders to translate business requirements into robust, scalable solutions. You will drive architectural decisions, contribute to industry-wide use cases, and design architectures for Agentic AI, Generative AI, and AI/ML.

Key responsibilities include collaborating with data engineers and scientists, setting engineering best practices, and identifying opportunities for generative AI to solve business problems. You will create proofs of concept (POCs) and proofs of value (POVs), write high-quality Python code, and stay updated on the latest AI trends and technologies. Mentoring AI engineers in best practices and problem-solving will also be a significant part of your role.

This senior role requires a minimum of 15 years of professional experience, with approximately 5-8 years dedicated to building and deploying AI solutions on-premises or on cloud platforms. A Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field is essential.

Technical expertise should include Agentic AI, Generative AI, MLOps, LLMOps, and AgentOps architecture. Experience deploying AI/ML solutions on Azure, AWS, and/or Google Cloud is crucial, along with proficiency in using and orchestrating LLM models. Strong skills in Python, pyspark, SQL, data modeling, and designing AI solutions using microservice architecture are required. Familiarity with responsible AI concepts, code versioning, MLFlow, and deployment processes on platforms like Kubernetes is also expected.