• Define end-to-end architecture for AI/ML and Gen AI, Agentic AI, MCP systems including data pipelines, model training/inference, and MLOps. • Serve as a strategic advisor / consultant to clients, leading solution design discussions, presenting AI/ML architectures, and representing 3Pillar in client-facing interactions to drive innovation and business value. • Architect scalable solutions using cloud-native AI tools (Azure ML, AWS SageMaker, or GCP Vertex AI). • Lead the integration of Generative AI into components / features leveraging LLMs into enterprise applications using APIs (OpenAI, Gemini and others) or open source models like LLama. • Design retrieval-augmented generation (RAG) systems with vector databases (Pinecone, Weaviate, FAISS and similar). • Guide teams on MLOps frameworks for CI/CD, model versioning, monitoring, and automated retraining. • Evaluate build-vs-buy decisions and benchmark AI tools/platforms. • Evaluate emerging technologies and trends in AI, ML, Gen AI, Agentic AI space and recommend adoption strategies. • Mentor technical teams and guide solution architects, AI/ML engineers. • Ensure ethical and responsible AI practices including bias detection, interpretability, and governance. • Act as a subject matter expert, providing guidance and mentorship to junior colleagues and strengthen the expertise in the organization. • Collaborate with leadership to align machine learning strategies with overall business objectives. • Represent the organization at industry conferences, workshops, and events. • Drive cross-functional collaboration to identify opportunities for applying machine learning to business challenges.

• Serve as a technical evangelist and trusted advisor for clients, shaping early-stage Data & AI conversations into compelling solution visions built on the Microsoft platform. • Partner closely with sales and account teams to drive opportunity qualification, solution strategy, and deal closure for Data & AI engagements. • Support technical discoveries with clients to understand business priorities, decision criteria, and value drivers, translating them into winning Data & AI architectures. • Lead design and present Data & AI solutions that clearly articulate business outcomes, technical feasibility, and ROI. • Provide executive-level technical depth in Azure with depth in data engineering, data architect, machine learning, or AI to influence client stakeholders from practitioner to C-level. • Support technical credibility throughout the sales cycle, including whiteboarding sessions, architecture reviews, proposals, and executive presentations. • Guide and mentor architects and data scientists to ensure solution consistency, technical excellence, and alignment with go-to-market strategy. • Identify and support opportunities to expand pipeline through innovation workshops, POVs, and targeted thought leadership aligned to Microsoft Data & AI offerings. • Ensure proposed solutions align with Microsoft best practices, security standards, and regulatory requirements. • Stay current on Microsoft Data & AI roadmaps and industry trends to continuously sharpen competitive positioning and client impact.

• Lead the design, development, and implementation of AI-based solutions • Architect and develop multi-agent systems and advanced AI task-execution frameworks • Design and integrate Graph Connectors, API Plugins, and Azure AI Search • Translate complex business requirements into scalable and secure technical architectures • Act as the technical lead, ensuring solution quality and performance • Collaborate with multidisciplinary teams to deliver solutions • Define and promote development best practices • Resolve complex issues throughout the software development lifecycle • Mentor and support other developers

• Map current-state processes, identify automation and optimization opportunities, and build end-to-end workflows that reduce manual work and improve consistency across GTM and operations • Develop and deploy AI agents, copilots, and automation tools that support daily operations — integrating AI into existing systems to enhance decision-making and execution, not as a bolt-on layer but as a first-class component • Connect tools, data sources, and workflows across the tech stack to ensure seamless data flow between systems and enable real-time insights and actions • Create scalable frameworks for how workflows are built, maintained, and improved — establishing best practices for automation governance and performance tracking so others can operate and extend what you build • Monitor workflow performance, iterate quickly based on user feedback and operational impact, and translate ambiguous stakeholder problems into structured, executable solutions