An AI Engineer contributes to the design and implementation of state-of-the-art AI solutions, leveraging techniques such as Language Models (LLMs) and generative AI. This role involves collaborating with stakeholders to identify business opportunities and define AI project goals, while staying updated with the latest advancements in generative AI.
Responsibilities
Contribute to the design and implementation of state-of-the-art AI solutions
Assist in the development and implementation of AI models and systems, leveraging techniques such as Language Models (LLMs) and generative AI
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, 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 and/or Master's degree in computer science, engineering, mathematics, or any other relevant subject from a reputable University
Minimum 3 years of experience in Data Science and Machine Learning
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
Millions of jobs, with real people getting hired every day
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1,000,000+
Tailored applications submitted
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
Expertise in data engineering, including data curation, cleaning, and preprocessing
Knowledge of trusted AI practices, ensuring fairness, transparency, and accountability in AI models and systems
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
Track record of driving innovation and staying updated with the latest AI research and advancements
Strong mathematical and quantitative skills including calculus, linear algebra, and statistics
Nice to have:
Apply trusted AI practices to ensure fairness, transparency, and accountability in AI models and systems
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
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
Experience with lean/agile software development
Benefits
13th salary and yearly bonus
Provident Fund
Private Medical and Life Insurance
Flexible working arrangements (hybrid work and flexible work schedule)
Friday afternoon off
EY Tech MBA and EY MSc in Business Analytics
EY Badges - digital learning certificates
Mobility programs (if interested to work abroad)
Paid Sick Leave
Paid Paternity Leave
Yearly wellbeing days off
Maternity, Wedding and New Baby Gifts
EY Employee Assistance Program (EAP) (counselling, legal and financial consultation services)
About the Company
EY is building a better working world by creating new value for clients, people, society and the planet, while building trust in capital markets. Enabled by data, AI and advanced technology, EY teams help clients shape the future with confidence and develop answers for the most pressing issues of today and tomorrow. EY teams work across a full spectrum of services in assurance, consulting, tax, strategy and transactions, and can provide services in more than 150 countries and territories.
