Getnet

• Define and lead the architecture of the enterprise Generative AI platform • Build and evolve the strategic roadmap for the AI platform • Design scalable architectures using LLMs, SLMs (Small Language Models), RAG, and multi-agent systems • Implement and optimize fine-tuning strategies for SLMs, including domain adaptation and resource-efficient approaches • Enable self-service AI capabilities for product and engineering teams • Create reusable frameworks, blueprints, and accelerators • Implement CI/CD pipelines for AI solutions • Ensure governance, security, observability, and regulatory compliance • Integrate data, models, agents, and applications in a unified manner • Define standards, best practices, and an AI-specific SDLC (evaluation, testing, and deployment) • Serve as a technical reference and foster cross-functional collaboration • Integrate agent protocols and interoperability: Agent-to-Agent Protocol (A2A), Model Context Protocol (MCP), Agent Payments Protocol (AP2, ACP), Verifiable Intent (VI) • Implement an agent engineering platform for observability, evaluation, and tracing • Develop frameworks for continuous improvement of agent quality • Enable no-code / low-code agents for scaled adoption by business teams

• Design and implement an AI orchestration layer integrated into the CI/CD pipeline. • Orchestrate AI agents and skills across all stages of the SDLC (design, development, testing, refactoring). • Define how AI sessions are created, managed, versioned, audited, and reviewed. • Design mechanisms to automatically provision per-project context. • Automate workflows from Jira tickets. • Embed AI orchestration into CI/CD pipelines. • Lead the initial deployment of Devin across the organization. • Define metrics to measure AI's impact on productivity, quality, and reliability. • Ensure all AI activity is observable, auditable, and aligned with corporate governance. • Act as the technical owner for AI within the SDLC.