Senior Staff Backend Engineer leading the design and delivery of AI-powered SaaS products at Kaseya. Collaborating with teams to embed data, ML, and AI capabilities into the product.
Responsibilities
Own the architecture and technical direction for key product areas in a multi-tenant SaaS platform.
Work with product, design, and data/ML teams to translate business problems into simple, robust technical solutions.
Drive the evolution of our system architecture (APIs, services, data flows, auth, tenancy, integrations) as the product and customer base scale.
Build and maintain backend services and APIs (REST/GraphQL/gRPC) that power those experiences.
Deliver backend features end-to-end including data schemas and business logic.
Collaborate with UX and product to ensure responsive and delightful product experiences.
Partner with data scientists and MLOps / platform teams to embed data, ML and AI capabilities into the product (recommendations, categorization, automation, routing, insights, LLM-powered workflows, etc.).
Design APIs, data contracts, and UX flows that make ML/AI features reliable, understandable, and safe for customers.
Ensure telemetry and feedback loops are in place so data/ML teams can measure performance, iterate models, and improve outcomes.
Help define and implement guardrails for AI features (fallbacks, explanations, error handling, permissions).
Champion operability: monitoring, alerting, logging, and runbooks for services you own.
Lead efforts to improve performance, scalability, and resilience of critical paths (e.g., onboarding, reporting, AI-assisted workflows).
Work with security and compliance to ensure features meet requirements around authentication, authorization, data privacy, and multi-tenancy.
Participate in and help evolve on-call / incident response processes as a technical leader.
Act as a technical mentor for multiple teams, raising the bar on code quality, reviews, testing, and design.
Lead technical design reviews and cross-team architecture discussions, especially where product, data, and ML intersect.
Help define engineering standards and best practices (API design, frontend patterns, error handling, observability, testing).
Partner with engineering management to shape roadmaps, staffing, and sequencing for major initiatives.
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