JOB SUMMARY

We're looking for a Senior Backend/Full-Stack Engineer to build and scale our enterprise SaaS platform, with a focus on third-party integrations and workflow orchestration. This is not an AI/ML role, while the platform includes an AI component (a Small Language Model, or "SLM"), this engineer will consume it as a service, not build or train it.

JOB RESPONSIBILITIES

• Design, build, and maintain backend/full-stack systems for our enterprise B2B SaaS platform
• Build and maintain adapters/integrations against third-party enterprise APIs, handling OAuth/SCIM, webhooks, rate limits, flaky vendor endpoints, and API versioning
• Design and develop event-driven systems, including queues, workflow orchestration, and idempotency handling — directly supporting our blueprint execution engine (a workflow engine)
• Implement and maintain enterprise-grade security and compliance features, including RBAC, SSO/SAML, audit logging, and tamper-evident records (our "Evidence Vault")
• Support self-hosted/on-prem deployments using Docker and Kubernetes
• Integrate with our SLM (Small Language Model) as a consumer service — working with structured outputs, retrieval-augmented workflows, and eval-aware development
• Take ownership of architectural decisions and technical direction, not just feature delivery
• Mentor other engineers and provide technical guidance across the team
• Review and push back on specs where appropriate, proposing alternative technical approaches

JOB QUALIFICATIONS:

• 5+ years of backend or full-stack development experience on enterprise B2B SaaS shipped to production (agency or portfolio-only work will not be considered)
• Strong background in a statically typed language; Rust proficiency preferred, or demonstrated ability to ramp up quickly in Rust
• Proven, hands-on experience building integrations/adapters against third-party enterprise APIs (OAuth/SCIM, webhooks, rate limiting, versioning)
• Experience with event-driven architecture: queues, workflow orchestration, idempotency
• Experience with enterprise security/compliance implementations: RBAC, SSO/SAML, audit logging, on-prem/self-hosted deployment (Docker, Kubernetes)
• Comfortable working with AI/LLM services as a consumer (structured outputs, retrieval, evaluation) — ML/AI engineering background is explicitly not required or expected
• Strong communication skills and comfort challenging specs or proposing alternative solutions
• Prior experience mentoring or providing technical leadership to other engineers

Originally posted on Himalayas