Job Summary

To manage a team of skilled solution specialists in preparing RPA solution and exercise independent judgement to propose/identify appropriate alternatives as an subject matter expertise in the domain.

Key Responsibilities

1. To Manage The Team Of Solution Architects And The Respective Solution Design Teams In Completing The Agreed Solution Modules/Projects For The Client As Per The Requirements
2. To Continuously Update Oneself With Strong Design Foundation Of Rpa And In-Depth Understanding Of Methodologies & Technologies
3. To Be Solely Accountable For All Aspects Of The Research And Design Facets Of The Rpa Solution Development Process
4. To Ensure All Tools And Automations Support Is Facilitated After Delivery According To The Established Automations Support
5. To Review All Suggested Rpa Solutions And Alternatives For Given Client Requirements

Skill Requirements

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Other Requirements

Job Description – Senior Principal Architect (Cloud & GenAI) Role Title Senior Principal Architect – Cloud, Data, AI/GenAI & Enterprise Transformation Purpose of the Role The Senior Principal Architect will serve as a strategic technology leader responsible for driving enterprise-scale cloud and GenAI transformation initiatives across BFS clients. The role bridges CxO-level advisory and deep engineering execution, ensuring scalable, secure, and compliant architecture aligned to enterprise standards and regulatory frameworks. Key Responsibilities 1. Executive Advisory & Architecture Governance Act as a trusted advisor to CIO/CTO/Chief Architects on cloud, data, and AI transformation strategy. Define enterprise architecture blueprints aligned with TOGAF and regulatory guidelines. Lead Architecture Review Boards (ARB) and ensure compliance with enterprise governance frameworks. Provide technology roadmaps aligned to multi-year transformation programs. 2. Cloud & GenAI Transformation Leadership Lead end-to-end delivery of cloud modernization, migration, and re-architecture programs. Drive adoption of Generative AI solutions including: Agentic AI workflows RAG-based architectures Enterprise knowledge bases Operationalize GenAI solutions into production at scale across business functions. 4. Cloud-Native & Distributed Systems Architecture Design multi-region, highly available, and scalable cloud-native architectures. Lead adoption of: Serverless & event-driven patterns Microservices and container-based platforms Ensure Zero Trust Security models and enterprise-grade resilience. 5. Responsible AI, MLOps & Compliance Establish Responsible AI frameworks, including: Model governance and evaluation Bias mitigation and explainability PII protection and secure data handling Implement MLOps pipelines for continuous model lifecycle management. Core Competencies Enterprise Architecture: TOGAF, architecture governance, solution design Cloud Platforms: AWS (primary), multi-cloud strategy AI/GenAI: Bedrock, AgentCore, Lambda, LLMs, RAG, Agentic AI frameworks Data Platforms: Streaming (Kinesis/MSK), Databricks, Lakehouse Engineering Excellence: DevOps, CI/CD, Infrastructure as Code Security & Compliance: Zero Trust, data privacy, r