You will own the end-to-end system architecture—from conceptualization and high-level design to hands-on technical guidance and code reviews—ensuring that both platforms are scalable, modular, secure, and future-proof.
Key Responsibilities
1. Architecture & Design Leadership
• Lead the overall solution architecture for both platforms—defining component boundaries, data flows, API contracts, and cloud/service integrations. • Architect a microservices-based, modular, and API-first backend for both the no-code builder and MLOps platform, ensuring reusable services and seamless integration.
- Design for multi-tenancy, role-based access control (RBAC), localization (i18n/l10n), and strong data privacyfrom the outset.
- Define best practices for real-time collaboration (e.g., CRDT/OT engines) and ensure robust support for version control, rollback, and undo/redo across both platforms.
- Lead architectural decisions for LLM/AI agent integration, including scalable inference pipelines, caching, prompt management, and agent orchestration.
- Select and define technical standards for cloud infrastructure, containerization (K8s/Docker), model training/deployment (MLflow, Kubeflow, Airflow, etc.), CI/CD, and monitoring.
2. Technical Oversight & Execution
- Provide technical direction, code reviews, and mentorship to backend, frontend, DevOps, MLOps, AI/ML, and NLP teams—ensuring adherence to architecture and coding standards.
- Oversee the implementation of AI/ML/NLP pipelines, ensuring modularity and smooth integration with product features (e.g., drag-and-drop builder, agent workflows).
- Drive decisions around data management: model/data versioning, lineage, cataloging, storage, and secure data flows.
- Ensure API gateway design, external integration readiness, and internal service discoverability.
- Own the architectural roadmap for third-party integrations (e.g., Figma, Zapier, cloud vendors, payment gateways).
3. Performance, Scalability & Security
- Define and enforce performance optimization strategies: API efficiency, database sharding/replication, horizontal scaling, caching layers.
- Architect for high availability, disaster recovery, automated backups, and graceful failover.
- Ensure SOC2/GDPR/ISO compliance readiness, robust audit logging, secrets management, and secure tokenization.
- Oversee secure implementation of user management, authentication (OAuth2/SAML/MFA), and authorization.
4. Cross-Platform Integration & Product Synergy
- Design a shared foundation so that both platforms can leverage common services, data models, user management, and notification systems.
- Architect for future extensibility: plugins, third-party module integration, and easy onboarding of new AI/ML workflows or UI blocks.
- Partner closely with Product Management, UX/UI, and PhDs (AI/ML, NLP, HCI) to align technology with user and business needs.
5. Delivery, Documentation & Continuous Improvement
- Lead architecture sessions, whiteboarding, and design reviews with cross-functional teams.
- Deliver comprehensive architecture documents, sequence diagrams, and decision logs.
- Define technical KPIs: system uptime, API latency, model inference time, error rates.
- Proactively identify architectural risks and bottlenecks—recommending refactors or redesigns as needed.
- Champion a culture of innovation, security, quality, and continuous improvement.
Required Experience & Skills
- Minimum 10 years in software engineering; 4+ years architecting complex cloud native, AI/ML-powered products.
- Deep experience with both no-code/low-code builder platforms and/or MLOps/AI agentic systems.
- Proven track record of designing microservices/SOA, multi-tenant architectures, and real-time collaborative systems.
- Hands-on expertise with cloud infra (AWS/GCP/Azure), Kubernetes, Docker, CI/CD, and ML pipelines(MLFlow, Kubeflow, Airflow).
- Strong AI/ML and LLM system design understanding, including model lifecycle, serving, and real-time inference.
- In-depth knowledge of security, compliance, RBAC, OAuth2/SAML, and data privacy.
- Experience architecting and scaling systems for millions of users and/or enterprise clients.
• Excellent communication, mentorship, and leadership skills. • Comfort with documentation, architectural diagrams, and driving technical decisions.
Desirable Skills
- Experience integrating with Figma, Zapier, or similar design/workflow tools.
- Exposure to blockchain-based data security or tokenized reward systems.
- Prior work in AI-powered drag-and-drop builders, agent orchestration, or similar SaaS products.
- Experience working with distributed teams, startup environments, and fast-paced delivery.
What Success Looks Like
- Both platforms launch on time, with high reliability, modularity, and extensibility.
- Minimal rework/tech debt post-MVP due to robust, forward-thinking architecture.
- Seamless integration of AI/ML/NLP features, with fast performance and easy onboarding for developers and end-users.
- Scalable foundations to rapidly support new features, integrations, and enterprise demands.
Reporting & Collaboration
- Reports to Head Deep Tech & Applied AI
- Directly mentors all technical leads (Backend, DevOps, MLOps, AI/ML, NLP, Frontend, Security).
- Works closely with Product, UI/UX, PhDs, and QA.
Why Join Us?
- Be the key architect for two next-gen AI products, working with a world-class team and ample budget.
- Massive ownership, impact, and visibility.
- Opportunity to shape market-defining platforms used by enterprises and innovators globally.
