Solution Architect - AI & MLOps Platforms

Architecture & Design Leadership

• Define and lead the overall solution architecture, component boundaries, APIs, data flows, and cloud/service integrations.
• Architect microservices-based, API-first backends for both platforms.
• Design for multi-tenancy, RBAC, internationalization, and privacy-by-design.
• Implement collaboration frameworks (e.g., CRDT/OT), version control, and rollback systems.
• Lead architecture for LLM/AI agent integration: inference pipelines, caching, prompt orchestration.
• Set technical standards for cloud infrastructure, containerization (Docker, K8s), and ML ops tools (MLflow, Airflow, etc.).

Technical Oversight & Execution

• Provide mentorship and code/architecture reviews across backend, frontend, DevOps, and AI teams.
• Oversee integration of AI/ML pipelines into product features.
• Drive decisions on data lifecycle: versioning, lineage, cataloging, and secure flows.
• Ensure effective API gateway design and service discoverability.
• Define the roadmap for third-party integrations (Zapier, Figma, payments, cloud).

Performance, Scalability & Security

• Architect for scale: sharding, caching, horizontal scaling, and efficient API performance.
• Ensure high availability, disaster recovery, and graceful failover mechanisms.
• Implement compliance frameworks (SOC2, GDPR, ISO), secure tokenization, audit logging.
• Oversee authentication/authorization: OAuth2, SAML, MFA, RBAC.

Cross-Platform Integration & Product Synergy

• Create a shared foundation across platforms—user management, services, and data models.
• Enable plugin architecture and future extensibility.
• Collaborate with Product, UX, and research teams (AI/ML, NLP, HCI) to align tech with product goals.

Delivery, Documentation & Continuous Improvement

• Run whiteboarding sessions, design reviews, and architecture alignment meetings.
• Maintain architecture documents, diagrams, and decision logs.
• Track system KPIs: uptime, latency, inference time, error rates.
• Proactively identify and resolve architectural risks or tech debt.
• Foster a culture of innovation, quality, security, and agility.

Requirements & Skills

• 10+ years of software engineering experience, with 4+ years in complex cloud-native architecture.
• Proven success designing microservices/SOA for scalable AI/ML platforms.
• Strong knowledge of no-code builders or MLOps systems.
• Hands-on with:
• Cloud: AWS, GCP, Azure
• Containers & Orchestration: Docker, Kubernetes
• ML Pipelines: MLFlow, Airflow, Kubeflow
• Security: OAuth2, SAML, RBAC, GDPR compliance
• CI/CD, Monitoring, Logging
• Experience integrating LLMs, prompt management, and agent orchestration.
• Built systems handling millions of users and/or enterprise-scale workloads.
• Excellent communication, documentation, and leadership skills.

Desirable Skills

• Integration experience with tools like Figma, Zapier
• Familiarity with blockchain-based data security or token systems
• Prior experience in AI-powered drag-and-drop builders or agentic SaaS tools
• Experience in distributed, fast-paced startup environments

Success Metrics

• Robust MVP launch with minimal tech debt
• High system availability, extensibility, and reliability
• Smooth AI/ML feature integration with low-latency inference
• Rapid onboarding of developers and users with scalable architecture