Principal Architect

Overview

Principal Architect role setting enterprise AI architecture direction across Finastra products, platforms and business segments, with secondary focus on cloud and SaaS architecture.

What you'll do

• Set the enterprise AI architecture vision, principles, standards, reference patterns, capability roadmaps, and technology strategy across multiple products, platforms, and business segments.
• Provide authoritative architectural direction for generative AI, RAG, agentic AI, model integration, orchestration, prompt and context engineering, and AI evaluation.
• Sponsor and lead strategic AI research, technology evaluations, and Proofs of Concept, translating validated outcomes into reusable, production-grade capabilities with measurable business value.
• Establish enterprise guardrails for responsible AI, security, privacy, model risk, data governance, observability, auditability, and human oversight in regulated environments.
• Shape cloud-native and SaaS architecture strategy for multi-tenancy, tenant isolation, scalability, resilience, availability, metering, subscription models, and cost management.
• Create and drive adoption of reference architectures, reference implementations, patterns, libraries, accelerators, and engineering guidance across product and platform teams.
• Govern and review strategic architecture and design proposals for alignment with AI strategy, cloud and SaaS principles, enterprise standards, security, regulatory obligations, and long-term sustainability.
• Influence executive and senior technical decisions across architecture, engineering, product, data, security, cloud platforms, governance, and business leadership.
• Develop senior architects and principal engineers, and lead enterprise architecture forums, technical councils, workshops, demonstrations, hackathons, and global knowledge-sharing communities.

What you'll need

• 12 years of progressive experience in a corporate IT environment or an IT consulting company.
• At least 3 years in a senior role.
• 10+ years of experience in building, integrating and delivering large enterprise scale solutions using web based technologies.
• 10+ years of experience designing applications.
• 10+ years of experience delivering solutions in an agile environment.
• Artificial Intelligence architecture and strategy: enterprise-wide AI target architecture, principles, standards, investment roadmaps, technology portfolio decisions, and adoption strategy across products and business segments.
• Generative AI and LLM systems: strategic direction for LLM integration, prompt and context engineering, structured outputs, model routing, grounding, safety, and production controls at enterprise scale.
• Retrieval-Augmented Generation: enterprise standards for search, embeddings, vector retrieval, chunking, ranking, grounding, citations, authorization-aware access, and measurable retrieval quality.
• Agentic AI: enterprise patterns and governance for agent orchestration, tool use, workflow design, memory, planning, permissions, human approval, monitoring, and failure containment.
• AI platforms and integration: shared model gateways, AI services, APIs, event-driven integration, data pipelines, reusable platform capabilities, and consistent embedding of AI across products.
• AI evaluation and observability: enterprise evaluation strategy spanning quality, relevance, groundedness, safety, latency, cost, drift, telemetry, red-teaming, and continuous assurance.
• Responsible AI and governance: organization-wide security, privacy, regulatory alignment, model risk, data governance, explainability, auditability, accountability, and human oversight.
• Cloud-native architecture using Microsoft Azure or comparable hyperscale platforms, including containers, Kubernetes, serverless, managed services, infrastructure automation, and platform engineering.
• SaaS architecture covering multi-tenancy, tenant isolation, identity, configuration, extensibility, metering, subscription management, data residency, and zero-downtime evolution.
• Distributed systems, APIs, event-driven integration, data platforms, observability, reliability, performance, disaster recovery, operational readiness, and cross-product interoperability.
• FinOps, capacity planning, AI and cloud cost optimization, security architecture, compliance, DevSecOps, CI/CD, and platform operating models.

Details

• Location: Bengaluru.
• Hybrid working arrangements.