Principal Architect - AI & Full Stack

Principal Architect - AI & Full Stack

Experience: 8-12 years

Location: Bangalore / Ahmedabad / Jodhpur

Role Overview:

We are looking for a strong hands-on, forward-thinking Technical pioneer who can split their time between two critical mandates:

1. 50% Solution Architecture & Client-Facing Technical Engagement– Architect scalable full-stack, cloud-native, AI-enabled, and multi-tenant solutions across client engagements, with strong focus on security, scalability, configurability, robustness and long-term platform sustainability.
2. 50% AI Innovation & Strategic Growth –Lead AI/GenAI POCs, define reusable AI capabilities, and establish modern AI-assisted and Vibe Coding-based engineering practices that enable rapid, continuous, and cost-effective delivery.

Key Responsibilities

1. Solution Architecture & Technical Engagement (50%)

• Architect scalable full-stack applications using React, Node.js, Python, Go, and modern cloud-native technologies.
• Design and evolve multi-tenant /multi-brandSaaS architectures with strong tenant isolation and configuration-driven capabilities, minimizing tenant-specific custom development.
• Establish reusable, configuration-driven architecture for UI, workflows, business rules, validations, integrations, permissions, feature flags, and tenant-specific requirements.
• Define API-first, event-driven, microservices/modular architectures and integration patterns based on business and scalability requirements.
• Govern architecture quality and technical decisions, ensuring systems remain robust, scalable, secure, maintainable, and production-ready while supporting continuous onboarding of new tenant requirements.
• Define Vibe Coding best practices covering requirement decomposition, architecture-first development, AI-assisted coding, context management, code review, testing, security validation, documentation, and human-in-the-loop governance.
• Establish and govern code quality, engineering standards, architecture review, testing, security, and best-practice adherence across the complete development and deployment lifecycle.
• Maintain strong hands-on awareness of the complete development and deployment lifecycle and guide teams on design patterns, coding standards, technical debt reduction, DevOps, and cloud-native engineering practices.
• Define modern deployment architectures using CI/CD, Infrastructure as Code, containers, Kubernetes, GitOps, automated testing, and progressive delivery.
• Support solution planning, architecture reviews, pre-sales, and client-facing technical discussions.
• Act as a technical point of contact for quick customer/team handoffs, clarifications, and architectural alignment when required.
• Collaborate effectively with engineering and technology teams across different geographies and locations.
• Mentor senior and mid-level engineers and conduct technical design and architecture reviews.

1. AI Innovation, POCs & Practice Building (50%)

• Architect and build AI/GenAI solutions including LLMs, model routing, embeddings, vector databases, hybrid search, RAG, tool/function calling, MCP, agent orchestration, guardrails, and AI observability.
• Lead AI POCs from business use-case discovery and architecture through prototype, evaluation, integration, and production-readiness assessment.
• Bring practical experience in implementing, deploying, and operationalizing AI/GenAI solutions in production environments.
• Build reusable AI services and accelerators that can be consumed across multiple products, clients, and tenants rather than developing isolated project-specific solutions.
• Define configuration-driven AI capabilities including tenant-specific models, prompts, tools, knowledge sources, policies, workflows, and AI features.
• Define and govern the architecture of the semantic layer, ontology, knowledge representation, and related knowledge/semantic architecture required to support AI-enabled applications and intelligent workflows.
• Drive Vibe Coding-based application development and establish reusable Agentic AI-assisted development workflows, templates, prompts, skills, coding agents, and engineering accelerators.
• Identify opportunities to use AI agents across the SDLC, including requirement analysis, architecture, coding, code review, testing, debugging, documentation, migration, and DevOps automation.
• Identify opportunities to apply AI to improve engineering productivity, automate business processes, reduce operational overhead, and create new client-facing capabilities.
• Collaborate with leadership to define the AI technology roadmap, reusable accelerators, reference architectures, and AI Center of Excellence capabilities.
• Track emerging AI technologies and evaluate their applicability across client engagements and industry-specific use cases.

Additional Responsibilities

• Identify business opportunities for modernization, platform reuse, AI-led engineering transformation, account expansion, and new AI-enabled business solutions.
• Conduct internal workshops, guide upskilling plans, and help build a pipeline of AI-savvy engineers.
• Participate in marketing and sales enablement, including client demos, case studies, and proposal support.