Senior Full-Stack Software Engineer

n this role you’ll:

Build and run the full-stack observability platform, design, develop, and maintain end-to-end applications for metrics, logs, and traces, covering both frontend and backend

Develop telemetry ingestion and API services, building backend services/APIs that ingest, process, store, and expose telemetry data reliably and at scale

Deliver user-friendly visualisation experiences, create responsive dashboards and UI workflows that help engineers explore, correlate, and act on observability signals quickly

Integrate with the observability ecosystem and connect the platform with tools and data sources such as Prometheus, Grafana, Splunk, OpenTelemetry, and event platforms like Kafka

Engineer reliable real-time streaming pipelines to build and integrate Kafka Streams / Kafka Connect pipelines to move telemetry through the platform with appropriate throughput and latency

Ensure resilience, delivery semantics, operational robustness, implement error handling, delivery guarantees, retries, and DLQ patterns; design for recoverability and safe failure modes

Embed AI/ML-driven intelligent observability features whilst designing and integrate capabilities like anomaly detection, forecasting, predictive alerting, noise reduction, and automated root-cause analysis into the product

Operationalise LLM capabilities with strong AI engineering and MLOps, and deliver production-grade LLM features (NLQ over telemetry, summarisation, incident triage, on-call copilots) using prompt engineering, RAG, evaluation, and guardrails; collaborate with DS/ML teams on model versioning, monitoring, retraining, and drift detection, including low-latency inference in streaming/serving paths

 

To be successful in this role you should meet the following requirements:

Strong experience in full-stack software engineering (production-grade) – proven delivery of end-to-end applications in live environments

Backend development & API design – strong in Java/Python/Go/Node.js plus RESTful API design and integration patterns

Good experience of data visualisation / observability UI experience with Grafana panels, D3.js, charting libraries, and data-driven dashboards

Applied AI/ML engineering in production – building or integrating AI capabilities (e.g., anomaly detection, forecasting, features) including familiarity with LLM APIs, prompting/RAG/embeddings/vector DBs, and the model lifecycle/MLOps basics

Frontend engineering background JavaScript/TypeScript with React/Angular/Vue, building performant, maintainable UIs

Solid understanding of SQL/NoSQL and handling time-series storage/querying concepts

Observability concepts & tooling, practical knowledge of metrics, logs, traces, and how to instrument/consume them

Hands-on integration with Kafka (or equivalent streaming platforms)