About the role
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 standardize 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).