Staff Engineer (Backend & Distributed Systems) - Agiloft

Job Responsibilities

DATA MODEL & PLATFORM ARCHITECTURE

• Own the canonical data models and service boundaries in your domain.
• Design models and boundaries enabling independent systems to evolve in parallel without duplicating logic or creating competing implementations.
• Evolve the platform through additive, backward-compatible changes that introduce new capabilities without disrupting existing behavior.
• Own the architecture of one or more critical platform domains, ensuring design integrity, scalability, reliability, security, and long-term technical health.

DISTRIBUTED SYSTEMS & CLOUD ARCHITECTURE (AWS)

• Design AWS-native, event-driven, and asynchronous services that scale on demand, degrade gracefully under load, and remain cost-efficient.
• Architect the backend to enable and support AI/ML workloads at scale, focusing on performance, scalability, and cost efficiency.
• Act as the architectural steward for these services, ensuring they remain scalable, resilient, secure, and maintainable.
• Drive cloud-native design principles, including stateless services, managed-service adoption, and failure-tolerant, self-healing architectures.

SECURITY & MULTI-TENANCY

• Treat tenant isolation, authentication, and authorization as first-class design concerns across every service and API.
• Design secure authentication and token-handling flows, permission-aware data access, and safe credential management for an enterprise, multi-tenant platform.
• Design for correct, isolated behavior across tenant, environment, and data-residency boundaries, explicitly reasoning about security posture and blast radius.

TECHNICAL LEADERSHIP & CROSS-FUNCTIONAL INFLUENCE

• Partner with product and engineering leaders to translate customer and business needs into platform architectures that solve problems.
• Lead the technical design of major platform initiatives end-to-end, driving alignment across teams with differing priorities and incentives.
• Integrate diverse stakeholders and project teams around a shared technical direction, navigating functional interests with strong interpersonal skills.
• Produce clear, rigorous design documents, service contracts, and acceptance criteria that others can review, build on, and trust.
• Use AI-assisted tools to accelerate and sharpen design and implementation, applying judgment to amplify productivity without compromising quality.
• Lead by example in applying AI-assisted engineering tools to accelerate architecture, design, implementation, and testing.

API DESIGN

• Design clean, versioned API contracts on top of the platform’s data models, including high-scale bulk and asynchronous operations, with deterministic behavior, idempotency, structured error handling, and well-defined limits.
• Establish contract tests and permission-enforcement validation as first-class parts of every API.

DELIVERY & ENGINEERING EXCELLENCE

• Own initiatives end-to-end from design through release, with accountability for product quality, reliability, security, observability, performance, scalability, usability, and cost.
• Champion modern practices: infrastructure-as-code, automated testing, continuous delivery, and observability-by-default.
• Lead platform-modernization and refactoring initiatives while maintaining delivery velocity and improving the developer experience.
• Identify and drive improvements to how the engineering organization designs and delivers.
• Contribute across the stack when needed, including UI layers, with backend remaining the primary focus.

ORGANIZATIONAL IMPACT & MENTORSHIP

• Serve as a technical multiplier, unblocking teams, accelerating architectural decisions, and reducing long-term complexity.
• Partner with CloudOps, Security, MLOps, and integration teams to define reliability targets, operational-readiness standards, and clean interfaces.
• Mentor senior and mid-level engineers, raising the bar for data-model and systems design, security, and design-document quality.
• Stay ahead of the fast-moving industry, particularly the rapid evolution of AI capabilities, and drive pragmatic adoption where it delivers clear business value.