EY - GDS Consulting - AIA - Gen AI - Senior

Senior AI Engineer

Role Overview:

1–2 years of direct experience in generative and agentic AI

agentic capabilities

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.
  

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