Artificial Intelligence and Machine Learning Backend Engineer

AI / ML Engineer

ROLE OVERVIEW As a Backend Engineer on the AI Ops platform, you will design and build the server-side systems that underpin all 14 platform modules - from real-time incident ingestion and AI model inference endpoints, to FinOps data aggregation pipelines and multi-tenant SLA management APIs. You will work closely with the Engineering Lead and AI/ML Engineer to create the data and API backbone that powers every frontend view. Two positions are available, with engineers aligned to different capability layers during parallel build sprints. KEY RESPONSIBILITIES Design and build RESTful APIs for all platform modules following OpenAPI specifications agreed with the Engineering Lead Implement multi-tenant data isolation architecture ensuring strict client data separation Build real-time event ingestion pipelines capable of processing 2.4M+ events per day Develop integrations with observability stack (Prometheus, Grafana) for Digital Twin data feeds Build backend services for the Incident Control module: AI root cause enrichment, resolution workflow, and escalation routing Implement the Predictive Models API layer: model registry, accuracy score feeds, and inference request handling Develop FinOps data aggregation services: cloud spend normalisation, baseline comparison, and trend calculation Build SLA tracking services: attainment calculation, breach detection, and at-risk client alerting Implement authentication, RBAC, and SSO/SAML integration for the Tenants and Integrations modules Write comprehensive unit, integration, and contract tests with high coverage targets Participate in architecture reviews, API contract design sessions, and code reviews EXPECTATIONS Deliver APIs that meet sub-100ms response time targets on primary dashboard data endpoints Maintain 99.9% API availability across all platform services Ensure all data endpoints support real-time WebSocket or SSE streaming for live dashboard updates Write clear OpenAPI documentation for every endpoint before frontend integration begins Follow security-first engineering practices - no sensitive data exposure, all inputs validated Produce services that are observable by default: structured logging, distributed tracing, health checks SKILLS & COMPETENCIES Technical Skills Strong proficiency in Node.js (TypeScript) and/or Python for microservice development RESTful API design and OpenAPI 3.0 specification authorship Deep knowledge of PostgreSQL: schema design, query optimisation, indexing, and migrations Time-series database experience: InfluxDB, TimescaleDB, or Prometheus remote write patterns Event streaming: Kafka, RabbitMQ, or AWS SQS/SNS for high-throughput event pipelines Kubernetes deployment: writing manifests, Helm charts, and understanding pod lifecycle Authentication patterns: JWT, OAuth 2.0, OIDC, SAML, and RBAC implementation Caching strategies using Redis for hot data paths and session management Testing: Jest/Vitest, Pact for contract testing, and k6 or Artillery for load testing Observability instrumentation: OpenTelemetry SDK integration and structured log output Functional & Soft Skills Strong API design instincts - thinks about contracts, versioning, and consumer experience first Clear technical communication for API documentation and cross-team collaboration Systematic debugging and root cause analysis mindset Collaborative engagement with frontend engineers on data shape and integration design Reliable delivery with proactive escalation of complexity or scope risks