EY GDS Consulting AIA AI Engineer GIG

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Job Description: AI Engineer GIG

Role Overview:

We are seeking a highly skilled and experienced GIG AI Engineers with a minimum of 4 years of experience including 12 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:

• Bachelors or Masters 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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