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, rapid-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

Skills: Docker, Microservices, Kubernetes, Cloud Architecture, MLops

Experience: 10.00-20.00 Years

📌 Solution Architect - AI & MLOps Platforms (Mumbai)
🏢 Yotta Infrastructure
📍 Mumbai