Usage of Conventional AI
Virtusa utilizes Artificial Intelligence (AI) technology for the purpose of conducting candidate pre-employment assessments. This AI may include both text-based and voice-based functionalities. The AI technology will have access to the data that you upload or provide during your interaction with the AI bot and will use such data solely for the purpose of performing assessment in accordance with Virtusa policies and practices. The AI is not used to substantially assist or replace discretionary employment decisions. Read our full employee privacy policy here.
Job description
Key Responsibilities
1. Core Generative Engine Development
•Architect and implement advanced Generative Adversarial Networks (GANs) for structured, tabular, time-series, and multimodal data.
•Develop Variational Autoencoders (VAEs) and latent variable models for representation learning and synthetic population generation.
•Integrate Bayesian methods (Bayesian Networks, Bayesian hierarchical models, Bayesian deep learning) to incorporate uncertainty and domain priors.
•Design hybrid generative frameworks combining deep learning and probabilistic graphical models.
________________________________________
2. Synthetic Population & Simulation Modeling
•Build statistically consistent synthetic populations that preserve:
oMarginal distributions
oJoint relationships
oCorrelation structures
oCausal dependencies (where applicable)
•Implement scenario simulation and intervention modeling using probabilistic inference.
•Quantify model fidelity, privacy risk (re-identification risk), and bias.
________________________________________
3. Platform & API Enablement
•Expose generative capabilities via RESTful APIs.
•Design modular architecture to allow regional POD-based extensions.
•Build reusable model components to support evolving domain requirements.
•Ensure scalability, reproducibility, and version control of generative models.
________________________________________
4. Privacy, Compliance & Governance
•Implement differential privacy techniques where required.
•Ensure synthetic data meets regulatory constraints.
•Develop validation metrics for fidelity, utility, and privacy preservation.
________________________________________
5. Cross-Functional Leadership
Collaborate with product teams, domain SMEs, and business teams.
Translate domain requirements into probabilistic modelling strategies.
Mentor junior data scientists and establish modelling best practices.
Contribute to platform roadmap and long-term AI strategy.
Qualification
Key Responsibilities
1. Core Generative Engine Development
•Architect and implement advanced Generative Adversarial Networks (GANs) for structured, tabular, time-series, and multimodal data.
•Develop Variational Autoencoders (VAEs) and latent variable models for representation learning and synthetic population generation.
•Integrate Bayesian methods (Bayesian Networks, Bayesian hierarchical models, Bayesian deep learning) to incorporate uncertainty and domain priors.
•Design hybrid generative frameworks combining deep learning and probabilistic graphical models.
________________________________________
2. Synthetic Population & Simulation Modeling
•Build statistically consistent synthetic populations that preserve:
oMarginal distributions
oJoint relationships
oCorrelation structures
oCausal dependencies (where applicable)
•Implement scenario simulation and intervention modeling using probabilistic inference.
•Quantify model fidelity, privacy risk (re-identification risk), and bias.
________________________________________
3. Platform & API Enablement
•Expose generative capabilities via RESTful APIs.
•Design modular architecture to allow regional POD-based extensions.
•Build reusable model components to support evolving domain requirements.
•Ensure scalability, reproducibility, and version control of generative models.
________________________________________
4. Privacy, Compliance & Governance
•Implement differential privacy techniques where required.
•Ensure synthetic data meets regulatory constraints.
•Develop validation metrics for fidelity, utility, and privacy preservation.
________________________________________
5. Cross-Functional Leadership
Collaborate with product teams, domain SMEs, and business teams.
Translate domain requirements into probabilistic modelling strategies.
Mentor junior data scientists and establish modelling best practices.
Contribute to platform roadmap and long-term AI strategy.
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 30,000 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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