Solution Architect

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).