Infosys—Solution Architect / Platform Solution Architect

Job description

Description: • Define the overall Platform target architecture. • Own architecture decisions across application, data, AI, integration, security and infrastructure layers. • Design SaaS and client VPC deployment models. • Define module integration patterns and shared platform capabilities. • Ensure SAR/CTR, Regulatory Reporting Controls, RegIntel, Audit Evidence and AI Governance are built on reusable foundations. • Define tenant isolation, module entitlement and configuration architecture. • Review API contracts, data models, audit events and integration patterns. • Partner with DevOps on Azure architecture, environments, CI/CD and deployment patterns. • Partner with Security on RBAC, encryption, privacy, secrets, security logging and certification readiness. • Partner with AI/LLM Engineer on AI runtime, RAG, prompt/model governance and XAI patterns. • Partner with Data Engineer on canonical model, mapping, lineage and data quality. • Support architecture walkthroughs with client architects and internal review boards. Requirements: As a Platform / Solution Architect, you will own the end-to-end solution architecture for the platform, ensuring all modules are built on a scalable, secure, cloud-native, AI-governed and reusable platform foundation. The architect will align product roadmap, platform engineering, data architecture, AI services, security controls, integration strategy, deployment models and non-functional requirements. You will work with cloud engineers, DevOps engineers, backend engineers, data engineers, AI engineers, QA and security teams to enable flexible platform architecture across development, test, demo, pilot and production-like environments to make it compatible with client platforms. Responsibilities: • Define the overall Platform target architecture. • Own architecture decisions across application, data, AI, integration, security and infrastructure layers. • Design SaaS and client VPC deployment models. • Define module integration patterns and shared platform capabilities. • Ensure SAR/CTR, Regulatory Reporting Controls, RegIntel, Audit Evidence and AI Governance are built on reusable foundations. • Define tenant isolation, module entitlement and configuration architecture. • Review API contracts, data models, audit events and integration patterns. • Partner with DevOps on Azure architecture, environments, CI/CD and deployment patterns. • Partner with Security on RBAC, encryption, privacy, secrets, security logging and certification readiness. • Partner with AI/LLM Engineer on AI runtime, RAG, prompt/model governance and XAI patterns. • Partner with Data Engineer on canonical model, mapping, lineage and data quality. • Support architecture walkthroughs with client architects and internal review boards. Skills: • Minimum 10–15 years of experience in solution architecture, enterprise architecture, platform architecture, or cloud-native product architecture across financial products & solutions, specific to regulation & compliance implementations. • Strong experience designing secure, scalable, API-driven, data-intensive SaaS platforms using Azure services, databases, integration patterns, DevSecOps, observability, and RBAC. • Experience working in regulated industries, preferably banking, financial services, risk, compliance, RegTech, AML, KYC, or regulatory reporting platforms. • Hands-on experience using AI-assisted tools such as GitHub Copilot, Microsoft Copilot, Cursor, Azure OpenAI, Google AI Studio / Gemini, or equivalent for architecture, documentation, API design, backlog refinement, and test scenario generation. • Must understand responsible AI usage in regulated environments, including prompt hygiene, human review, source-controlled outputs, data privacy, and restrictions on using real client data. • Translate product roadmap and business requirements into scalable architecture blueprints, design decisions, API/data patterns, NFRs, and implementation-ready guidance for engineering teams. • Partner with product, engineering, DevOps, data, AI, security, QA, and SMEs to ensure the platform is secure, scalable, auditable, maintainable, and ready for SaaS / client VPC deployment.