Develop and implement generative AI models for various applications (text, image, audio, video).
Research & Application:
Research and apply cutting-edge techniques in deep learning, NLP, and computer vision.
Data Pipelines: Design and build robust data pipelines for training and evaluating large-scale generative models.
Performance Optimization: Optimize model performance, including reducing latency and improving accuracy.
Cross-Functional Collaboration: Collaborate with cross-functional teams (product, engineering, research) to define and deliver AI-powered solutions.
Architecture Selection: Evaluate and select appropriate generative AI architectures and frameworks.
Model Fine-Tuning: Fine-tune pre-trained models for specific use cases and domains.
Documentation:
Develop and maintain documentation for models, data pipelines, and deployment processes.
Production Monitoring: Monitor model performance in production and implement necessary updates and improvements.
Staying Current: Stay abreast of the latest advancements in generative AI and related fields.
Ethical Considerations: Address ethical considerations and biases in generative AI models.
Security Implementation: Implement security measures to protect sensitive data and models.
Prototyping & Experimentation: Prototype and experiment with new generative AI concepts and applications.
Tool Development: Contribute to the development of internal tools and libraries for generative AI.
Troubleshooting: Troubleshoot and resolve technical issues related to generative AI models and infrastructure.
Technical Skills: Programming: Proficiency in Python.
Deep Learning Frameworks: Expertise in TensorFlow and/or PyTorch.
Generative Models: Deep understanding of GANs, VAEs, and transformer models.
NLP: Strong knowledge of natural language processing techniques.
Computer Vision: Familiarity with computer vision concepts and libraries.
Cloud Computing: Experience with cloud platforms (AWS, Google Cloud, Azure) for AI deployment.
Data Processing: Experience with data preprocessing and feature engineering
Model Development
Develop and implement generative AI models for various applications (text, image, audio, video).
Research & Application:
Research and apply cutting-edge techniques in deep learning, NLP, and computer vision.
Data Pipelines: Design and build robust data pipelines for training and evaluating large-scale generative models.
Performance Optimization: Optimize model performance, including reducing latency and improving accuracy.
Cross-Functional Collaboration: Collaborate with cross-functional teams (product, engineering, research) to define and deliver AI-powered solutions.
Architecture Selection: Evaluate and select appropriate generative AI architectures and frameworks.
Model Fine-Tuning: Fine-tune pre-trained models for specific use cases and domains.
Documentation:
Develop and maintain documentation for models, data pipelines, and deployment processes.
Production Monitoring: Monitor model performance in production and implement necessary updates and improvements.
Staying Current: Stay abreast of the latest advancements in generative AI and related fields.
Ethical Considerations: Address ethical considerations and biases in generative AI models.
Security Implementation: Implement security measures to protect sensitive data and models.
Prototyping & Experimentation: Prototype and experiment with new generative AI concepts and applications.
Tool Development: Contribute to the development of internal tools and libraries for generative AI.
Troubleshooting: Troubleshoot and resolve technical issues related to generative AI models and infrastructure.
Technical Skills: Programming: Proficiency in Python.
Deep Learning Frameworks: Expertise in TensorFlow and/or PyTorch.
Generative Models: Deep understanding of GANs, VAEs, and transformer models.
NLP: Strong knowledge of natural language processing techniques.
Computer Vision: Familiarity with computer vision concepts and libraries.
Cloud Computing: Experience with cloud platforms (AWS, Google Cloud, Azure) for AI deployment.
Data Processing: Experience with data preprocessing and feature engineering
Key job details
About Virtusa
Teamwork, quality of life, professional and personal development: values that Virtusa is proud to embody. When you join us, you join a team of 27,000 people globally that cares about your growth — one that seeks to provide you with exciting projects, opportunities and work with state of the art technologies throughout your career with us.
Great minds, great potential: it all comes together at Virtusa. We value collaboration and the team environment of our company, and seek to provide great minds with a dynamic place to nurture new ideas and foster excellence.
Virtusa is an Equal Opportunity Employer. All applicants will receive fair and impartial treatment without regard to race, color, religion, sex, national origin, ancestry, age, legally protected physical or mental disability, protected veteran status, status in the U.S. uniformed services, sexual orientation, gender identity or expression, marital status, genetic information or on any other basis which is protected under applicable federal, state or local law.
Applicants may be required to attend interviews in person or by video conference. In addition, candidates may be required to present their current state or government-issued ID during each interview. All candidates must be authorized to work in the USA.
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