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. AI Architecture & Strategy
GenAI Roadmap: Define the reference architecture for Large Language Models (LLMs) and RAG (Retrieval-Augmented Generation) systems, selecting the right mix of open-source (e.g., Llama, Mistral) and proprietary models.
Scalable Infrastructure: Architect end-to-end MLOps pipelines on cloud platforms (Azure/AWS) to ensure high availability and low latency for real-time banking applications.
Tech Stack Decisions: Lead decisions on vector databases, orchestration frameworks (LangChain/LlamaIndex), and inference engines.
2. AI Governance & Risk Management
Responsible AI Framework: Establish and enforce policies for Fairness, Accountability, and Transparency (FAT). Implement guardrails to detect hallucinations, bias, and toxic content in GenAI outputs.
Regulatory Compliance: Ensure all AI solutions align with evolving global standards (e.g., EU AI Act, NIST AI RMF) and banking-specific regulations (Central Bank guidelines).
Model Risk Management: Oversee the validation lifecycle of models, ensuring robust documentation, versioning, and auditable lineage.
3. Leadership & Collaboration
Stakeholder Management: Act as the technical advisor to C-suite executives, translating complex AI risks and opportunities into business language.
Team Mentorship: Guide a team of Senior Data Scientists and ML Engineers, fostering a culture of "Secure by Design" development.
Requirements
Experience: 10–12 years of total IT experience, with at least 4–5 years dedicated to AI/ML Architecture and 2+ years in AI Governance/Risk.
Technical Mastery:
o Deep understanding of Transformer architectures, LLM fine-tuning, and Agentic Workflows.
o Proficiency in Python and ML frameworks (PyTorch, TensorFlow).
o Hands-on experience with Governance tools (e.g., TruLens, Arize AI, or custom bias detection scripts). Domain Expertise: Strong background in BFSI (Banking, Financial Services, Insurance) is mandatory. You must understand data privacy (GDPR/NDMO) and PII protection.
Certifications: Certifications in Cloud Architecture (Azure Solutions Architect) or AI Governance are highly preferred.
Qualification
Key Responsibilities
1. AI Architecture & Strategy
GenAI Roadmap: Define the reference architecture for Large Language Models (LLMs) and RAG (Retrieval-Augmented Generation) systems, selecting the right mix of open-source (e.g., Llama, Mistral) and proprietary models.
Scalable Infrastructure: Architect end-to-end MLOps pipelines on cloud platforms (Azure/AWS) to ensure high availability and low latency for real-time banking applications.
Tech Stack Decisions: Lead decisions on vector databases, orchestration frameworks (LangChain/LlamaIndex), and inference engines.
2. AI Governance & Risk Management
Responsible AI Framework: Establish and enforce policies for Fairness, Accountability, and Transparency (FAT). Implement guardrails to detect hallucinations, bias, and toxic content in GenAI outputs.
Regulatory Compliance: Ensure all AI solutions align with evolving global standards (e.g., EU AI Act, NIST AI RMF) and banking-specific regulations (Central Bank guidelines).
Model Risk Management: Oversee the validation lifecycle of models, ensuring robust documentation, versioning, and auditable lineage.
3. Leadership & Collaboration
Stakeholder Management: Act as the technical advisor to C-suite executives, translating complex AI risks and opportunities into business language.
Team Mentorship: Guide a team of Senior Data Scientists and ML Engineers, fostering a culture of "Secure by Design" development.
Requirements
Experience: 10–12 years of total IT experience, with at least 4–5 years dedicated to AI/ML Architecture and 2+ years in AI Governance/Risk.
Technical Mastery:
o Deep understanding of Transformer architectures, LLM fine-tuning, and Agentic Workflows.
o Proficiency in Python and ML frameworks (PyTorch, TensorFlow).
o Hands-on experience with Governance tools (e.g., TruLens, Arize AI, or custom bias detection scripts). Domain Expertise: Strong background in BFSI (Banking, Financial Services, Insurance) is mandatory. You must understand data privacy (GDPR/NDMO) and PII protection.
Certifications: Certifications in Cloud Architecture (Azure Solutions Architect) or AI Governance are highly preferred.
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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