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