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We are seeking an AI-native Solution Architect with deep expertise in Node.js ecosystems, microservices architecture, and no-code/low-code platforms to design scalable, resilient, and configurable SaaS solutions. This role will be instrumental in building a generic, multi-tenant SaaS platform that enables rapid product development while ensuring performance, security, and high availability across a polyglot data infrastructure.
Responsibilities:
Define and drive architecture vision, standards, and governance practices.
Architect and scale multi-tenant SaaS platforms using microservices and event-driven design.
Design and standardise APIs, data architectures (SQL and NoSQL), and cloud-native/serverless systems.
Establish strong DevOps, CI/CD pipelines, and reusable engineering patterns.
Design and implement AI-native architectures using LLMs, embeddings, and vector databases.
Establish AI patterns such as RAG, copilots, and workflow automation.
Build prompt orchestration layers and AI gateway services.
Define AI governance, security guardrails, and responsible AI practices.
Drive AI-assisted engineering (code generation, documentation, automated reviews).
Lead adoption of AI-powered development tools and LLM-driven workflows.
Continuously evaluate and integrate emerging AI technologies.
Collaborate cross-functionally and mentor engineering teams.
Requirements:
8+ years in software engineering, including 3+ years in solution architecture.
Strong expertise in Node.js, microservices, event-driven systems, and REST/GraphQL APIs.
Experience with SQL and NoSQL databases (PostgreSQL, MySQL, MongoDB, Elasticsearch).
Hands-on experience with cloud platforms (AWS/GCP/Azure) and CI/CD and DevOps practices.
Familiarity with no-code/low-code platforms and workflow automation.
Experience with LLM APIs, RAG architectures, and AI integrations in production systems.
Must haves:
Active use of AI coding assistants (e. g., Claude, Cursor, Copilot) in daily development.
Strong prompt engineering skills for code generation, refactoring, and testing.
Experience working with agentic / multi-step AI workflows.
Understanding of AI limitations, risks, and hallucination handling.
Experience integrating LLMs into real-world applications (preferred).
Experience
8-12 yrs
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