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# Staff Site Reliability Engineer - AI Platform

- **Company:** Manychat
- **Location:** Amsterdam, Netherlands (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Continuous Integration, Failover, Reliability Engineering, OpenAI, Prometheus, AI Infrastructure, Delivery Pipeline, Large Language Models, Grafana, Caching, Rate Limiting, Discretization, AI Platforms, Kubernetes, Amazon Bedrock, LiteLLM, Machine Learning Operations, Terraform
- **Published:** November 12, 2026
- **Apply:** https://www.adzuna.nl/details/5961927406

## About the Role

* 5+ years in SRE / platform / infrastructure engineering, including production ownership at significant scale.
 * Hands-on experience operating LLM-backed systems in production: provider APIs (Bedrock, OpenAI, Anthropic, or similar), inference pipelines, self-hosted or managed model serving.
 * Deep cloud-native background: AWS, Kubernetes, Terraform/IaC, CI/CD.
 * Strong observability practice (Prometheus/Grafana, OpenTelemetry, or equivalent) and experience defining SLOs for non-deterministic systems.
 * Proven cost-optimization work: you can show where you cut cloud or inference spend and how you made cost visible.
 * Staff-level influence: you've set technical direction beyond your own team and brought others along

It would be great if you have:

 * Experience building or operating an LLM gateway/proxy (e.g., LiteLLM, Kong AI Gateway, custom).
 * Experience with GPU workload optimization, quantization, or serving frameworks (vLLM, TGI, Triton).
 * Experience with eval pipelines and quality monitoring for LLM outputs.

## Description

* Own reliability and performance of our AI infrastructure: AI Gateway, inference services, and integrations with Amazon Bedrock, Azure OpenAI, and other LLM providers.
 * Design and evolve the AI Gateway: routing, failover between providers, rate limiting, caching, and guardrails.
 * Build observability for AI systems: latency/throughput/error SLOs per model and provider, token-level metrics, quality and drift signals.
 * Drive cost optimization and FinOps for AI workloads: per-feature cost visibility, model right-sizing, caching strategies, provider mix.
 * Run capacity planning and incident response for inference services; write and improve runbooks and postmortems.
 * Scale AI expertise across the org: set standards, review designs, and coach teams shipping LLM-backed features.