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# classical SRE

- **Company:** Manychat Runs Ai
- **Location:** Barcelona, Spain (Remote available)
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Continuous Integration, Failover, Prometheus, Delivery Pipeline, Large Language Models, Grafana, Caching, AI Platforms, Kubernetes, Machine Learning Operations, Terraform
- **Published:** August 1, 2026
- **Apply:** https://www.jobleads.com/es/job/eafa802a9981872070f121e22d6a7a279

## 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, * 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., * Green-field ownership: the AI Platform is young; you'll shape its architecture, standards, and roadmap.
 * Real scale and real stakes: AI features sit in the critical path of customer-facing automation.
 * Direct partnership with the Head of Infrastructure; high autonomy and visibility.


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