EY - GDS Consulting - AIA - Gen AI - Senior at EY

Bidhannagar, kerala, India -

Full Time

Start Date

Immediate

Expiry Date

23 Feb, 26

Salary

0.0

Posted On

25 Nov, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Data Science, Machine Learning, NLP, Generative Modelling, Large Language Models, Agentic AI, Cloud Platforms, Computer Vision, Statistical Analysis, Feature Engineering, Optimization Techniques, Data Engineering, Collaboration, Problem Solving, AI System Architecture

Industry

Professional Services

Description

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. Senior AI Engineer Role Overview: We are seeking a highly skilled and experienced Senior AI Engineers with a minimum of 4 years of experience, including 1–2 years of direct experience in generative and agentic AI (e.g., GANs, VAEs, diffusion models, LLMs, and autonomous/agent-based architectures). This role will design, build and optimize advanced AI systems that not only produce content and automate tasks, but also embody agentic capabilities. Your foundation in machine learning and data science will underpin everything you do: applying your expertise in ML algorithms, data science methodologies, NLP, generative modelling, optimization techniques and AI system architecture to create cutting-edge AI models and systems. Responsibilities: Your technical responsibilities: Contribute to the design and implementation of state-of-the-art AI solutions. Assist in the development and implementation of AI models, and deploy advanced generative AI models for text, vision, audio, or multimodal applications Fine-tune and customize LLMs and generative models for domain-specific tasks, leveraging RLHF, transfer learning, and continual learning approaches Collaborate with stakeholders to identify business opportunities and define AI project goals. 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. Implement and optimize end-to-end pipelines for generative AI projects, ensuring seamless data processing and model deployment. 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. Collaborate with domain experts, stakeholders, and clients to understand specific business requirements and tailor generative AI solutions accordingly. Conduct research and evaluation of advanced AI techniques, including transfer learning, domain adaptation, and model compression, to enhance performance and efficiency. Establish evaluation metrics and methodologies to assess the quality, coherence, and relevance of generative AI outputs for enterprise industry use cases. 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. Requirements: Bachelor's or Master's degree in Computer Science, Engineering, or a related field. Minimum 4 years of experience in Python, Data Science, Machine Learning, OCR and document intelligence Strong foundation in machine learning and data science: statistical analysis, feature engineering, modelling (supervised/unsupervised), NLP, optimization techniques. Hands-on experience with generative modelling (GANs, VAEs, diffusion models) and large language models (LLMs). Familiarity with agentic AI concepts: designing agents that plan, act, interact, have memory/context, and orchestrate workflows. Proficiency in Python and relevant ML/AI frameworks (e.g., PyTorch, TensorFlow, Hugging Face). Ability to define and build data pipelines, work with structured/unstructured data, and apply data science best practices. Demonstrated ability to design and implement AI system architecture: modular, maintainable, scalable. Excellent problem-solving skills and the ability to work in a fast-moving, innovative environment. Strong communication and collaboration skills: able to interface with engineering, data science, product and business teams. 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. Experience with cloud platforms such as Azure, AWS, or GCP and deploying AI solutions in a cloud environment. Strong collaboration with software engineering and operations teams to ensure seamless integration and deployment of AI models. 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). Drive DevOps and MLOps practices, covering continuous integration, deployment, and monitoring of AI models. 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. Collaborate seamlessly with software engineering and operations teams for efficient AI model integration and deployment. Familiarity with DevOps and MLOps practices, including continuous integration, deployment, and monitoring of AI models. EY | Building a better working world EY exists to build a better working world, helping to create long-term value for clients, people and society and build trust in the capital markets. Enabled by data and technology, diverse EY teams in over 150 countries provide trust through assurance and help clients grow, transform and operate. Working across assurance, consulting, law, strategy, tax and transactions, EY teams ask better questions to find new answers for the complex issues facing our world today.

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Responsibilities

The Senior AI Engineer will design, build, and optimize advanced AI systems, focusing on generative and agentic capabilities. Responsibilities include developing AI models, collaborating with stakeholders, and ensuring compliance with data privacy and ethical considerations.