Vice President

Meet Our Team:

Pegasystems is seeking a VP of Generative AI Engineering to define and lead our enterprise GenAI strategy and product roadmap. In this pivotal leadership role, you will drive the vision, architecture, and execution of AI-powered capabilities that transform how our clients leverage the Pega platform. You'll oversee the end-to-end development lifecycle of GenAI initiativesfrom research and prototyping through production deploymentwhile building and leading a world-class global team of engineers and applied researchers.

As a key technical and strategic leader, you'll partner across Product Engineering, Go-To-Market, and executive stakeholders to embed cutting-edge AI capabilities (including LLM orchestration, RAG, and agentic systems) into enterprise workflows. You'll establish responsible AI governance, drive innovation in AI services, and serve as Pega's primary internal and external engineering evangelist for Generative AI.

This role requires at least 3 days in our Hyderabad or Bangalore office.

Picture Yourself at Pega:

This role requires a seasoned technology executive with deep expertise in AI/ML, proven success launching enterprise-scale AI products, and the ability to balance rapid innovation with enterprise-grade security, compliance, and governance requirements. You'll shape the future of AI-driven workflow automation and deliver measurable business value to global enterprise clients. You have already trialled multiple GenAI technologies in the last 2.5 years understanding the pitfalls and capabilties of different LLMs and approaches.

What You'll Do at Pega:

Strategic Leadership & Roadmap

Define and execute Pegas Generative AI vision and multi-year technical roadmap aligned with business goals. Identify market opportunities, prioritize investments, and set KPIs and governance for responsible AI. Drive innovation in LLM orchestration, RAG, and agentic architectures.

Engineering, Architecture & Technical Execution

Lead end-to-end development from research to deployment and optimization. Ensure architectural consistency, performance, security, and compliance. Implement best practices for MLOps, model evaluation, prompt management, and observability. Manage vendor ecosystem for cost and risk balance.

Collaboration & Go-To-Market

Serve as GenAI evangelist; partner with Product, Engineering, GTM, and Customer Success to embed AI into workflows. Create reference architectures and enablement assets to accelerate adoption. Align with Legal, Security, and Compliance on responsible AI.

Talent Development

Recruit and mentor a global team across engineering and research. Foster a culture of experimentation and learning. Establish career paths, knowledge sharing, and engineering excellence.

Thought Leadership & Market Engagement

Publish insights on GenAI trends and Pegasystems approach. Engage with analysts, academia, and industry leaders. Represent Pegasystems at conferences and in strategic customer dialogues.

Who You Are:

• 15+ years in software leadership, with 7+ years leading AI/ML initiatives.
• 5+ years leading AI/ML initiatives with direct experience in Generative AI (LLMs, RAG, agentic systems) and NLP. Proven track record launching and scaling AI-powered products/features at enterprise B2B scale.
• Deep technical expertise with modern AI stacks and enterprise SaaS architectures; strong knowledge of cloud-native deployments (e.g., microservices, Kubernetes, APIs).
• Experience leading globally distributed engineering teams (50+ members) and driving cross-functional alignment.
• Strong command of responsible AI, data privacy, security, and model governance in regulated environments.
• Exceptional communication, executive presence, and stakeholder management; ability to translate complex technology into clear business value.

What You've Accomplished:

Additional Preferred Qualifications

• Advanced degree (MS or PhD) in Computer Science, Artificial Intelligence, or a related field. Experience with Pega software.
• Demonstrated experience building research-oriented engineering teams and academic/industry partnerships.
• Experience establishing MLOps practices, model evaluation frameworks, and AI observability at scale.
• Familiarity with the AI vendor ecosystem and strategic build/buy/partner decision-making.
• History of thought leadership (publications, patents, conference speaking) in AI/ML or GenAI.