Job Description

Requirements:

• 8–10 years of experience in building production-grade backend systems, with clear ownership of system architecture.
• Strong proficiency in Python (FastAPI, SQLAlchemy, AsyncIO) or similar backend technologies.
• Deep expertise in PostgreSQL, including schema design, query optimization, migrations, and row-level security.
• Working knowledge of how LLMs and AI agents interact with backend systems, including constraints, feedback loops, and data governance.
• Strong experience in designing APIs at scale using GraphQL or REST.
• Hands-on experience with event-driven architectures using Kafka or comparable tools.
• Proven track record in designing multi-tenant systems or platform-level architectures.
• Strong understanding of distributed systems concepts such as consistency, fault tolerance, and observability.
• Experience working with Kubernetes and containerized systems.
• Strong written and verbal communication skills, including experience with design documents and architecture decision records.
• Experience with policy-as-code frameworks (OPA, Rego, ABAC), audit logging or tamper-evident systems, and contributions to open-source backend or data platforms.
• Exposure to AI/LLM systems or agent-based platforms and their integration patterns.
• Experience with air-gapped or on-premise deployments in restricted environments.

Responsibilities:

• Design and own backend architecture with focus on scalability, reliability, fault tolerance, and multi-tenant design.
• Build and implement the ontology layer, including PostgreSQL schema design (object types, relationships, properties, governance, and policy entities) with proper indexing and row-level security.
• Define and maintain API contracts using GraphQL or REST, ensuring secure tenant isolation, version control, and efficient data access.
• Design and manage event-driven communication across systems using Kafka or similar technologies.
• Develop and manage action workflows (e.g., propose simulate approve execute) with strong auditability, rollback support, and control mechanisms.
• Create reference implementations, review code, and define engineering standards for performance, testing, observability, and reliability.
• Lead and mentor backend engineers, conduct design reviews, and guide architectural decisions.
• Define service-level objectives (SLOs), perform load testing, and ensure system availability and resilience.
• Design and manage cloud-native deployment architectures, including networking, storage, scalability, and cost optimization across cloud, hybrid, and on-prem environments.
• Work closely with AI/ML teams to define safe and controlled interaction patterns between the ontology layer, agents, and LLM-based components.