Platform Engineer: Scalable AI Infra & Observability

Rensora AI; United Kingdom; Remote; GBP 90,000 - 120,000; Full time

Benefits offered by this job

Remote-first

Deep technical work

Equity

Learning budget

Cutting-edge AI systems

Job summary

Rensora AI is seeking a Platform Engineer to build the core infrastructure powering its AI agentic lifecycle platform. You will design distributed systems, implement CI/CD pipelines, and ensure secure, scalable operations across cloud environments.

You will collaborate with ML engineers and product teams to meet strict SLAs, multi-tenant isolation, and zero-downtime deployments. Remote-first culture with enterprise-scale responsibilities awaits.

Qualifications

• 4+ years in platform/infrastructure engineering or backend systems.
• Go or Python production distributed systems experience.
• Kubernetes deployment, networking, resource management and troubleshooting.
• Terraform and cloud (AWS preferred).
• Event-driven architectures with Kafka/NATS/RabbitMQ.
• Relational DBs (PostgreSQL) and caching (Redis) in production.
• Observability: Prometheus, logs, traces; SLO-driven ops.

Responsibilities

• Design core platform services for Evolis AI, including orchestration and state management.
• Build and maintain Kubernetes-based infra with Terraform for multi-cloud deployments.
• Develop event-driven architectures using message brokers to support real-time workflows.
• Implement comprehensive observability and alerting to meet enterprise SLAs.
• Design CI/CD pipelines with safe, auditable deployments and canary releases.
• Ensure multi-tenant isolation and security boundaries for compliance.

Tools

Kubernetes

Terraform

Kafka

NATS

RabbitMQ

PostgreSQL

Redis

Prometheus

ELK/Loki

SLO

Job description

Rensora AI is seeking a Platform Engineer to build the core infrastructure powering its AI agentic lifecycle platform. You will design distributed systems, implement CI/CD pipelines, and ensure secure, scalable operations across cloud environments.

You will collaborate with ML engineers and product teams to meet strict SLAs, multi-tenant isolation, and zero-downtime deployments. Remote-first culture with enterprise-scale responsibilities awaits.