Senior Software Engineer- SRE

Leadout Capital; Dublin; On-site; EUR 110,000 - 140,000; Full time

Job summary

Leadout Capital is seeking an experienced Site Reliability Engineer to drive reliability for large-scale, internet-facing services. You will lead incident response, design automation and self-healing systems, and build comprehensive observability platforms.

You will apply AI/ML to operations, collaborate with product teams, and mentor junior engineers while ensuring SLIs/SLOs are met and toil is reduced.

Qualifications

• 5+ years of experience in systems and software engineering for large-scale services.
• Strong knowledge of distributed systems and internet architectures.
• Proficiency in Python and Go with solid software practices.
• Hands-on with containerized architectures (Docker/Kubernetes) and workflow engines.
• Experience building observability platforms (Grafana/Prometheus/ELK).
• Background in incident management, on-call, and postmortems.
• AI-first approach to engineering and applying AI tools in ops workflows.

Responsibilities

• Lead incident detection, response, and resolution with root cause analysis.
• Design and implement automation platforms and self-healing systems.
• Architect production-grade observability solutions (monitoring, logging, tracing).
• Develop AI/ML-powered operations tools for anomaly detection and runbook automation.
• Collaborate with product and engineering to define SLAs/SLOs and mentor juniors.

Skills

Python

Go

Docker

Kubernetes

Observability

SRE principles

AI in operations

Incident management

Postmortems

CI/CD

Tools

Temporal

Airflow

Argo Workflows

Grafana

Prometheus

ELK

Splunk

Datadog

GitHub Copilot

Job description

The Experience

As an SRE, you will be a technical leader of the team driving Salesforce’s operational resilience by engineering solutions that blend automation, observability, and AI-powered platforms. You will not only respond to incidents but proactively design systems that prevent them, applying software engineering principles to operations to reduce toil and improve reliability at scale. By leveraging cutting-edge software engineering practices within SRE function and AI-driven insights, you will help transform how services are built, monitored, and operated — ensuring that Salesforce delivers always-on, high-performance experiences to customers worldwide.

Build and run reliable, scalable, and efficient systems by applying software engineering principles to operations. Our mission is to ensure services are highly available, performant, and resilient — while continuously improving the balance between operational work and engineering innovation.

• Reliability as the Priority: Ensure that systems meet defined Service Level Indicators (SLIs) and Service Level Objectives (SLOs), using error budgets to guide engineering and release decisions.
• Engineering for Operations: Apply software engineering practices — automation, monitoring, self-healing systems — to eliminate toil and improve operational efficiency.
• Incident Management: Lead the coordinated response to incidents as an Incident Commander, drive fast recovery (low TTR), and ensure lasting improvements through blameless postmortems.
• Continuous Improvement: Identify and remove sources of toil, enhance observability, and optimize systems to reduce Time to Detect (TTD) and Time to Restore (TTR).
• Collaboration with Development: Partner with product and engineering teams early in the lifecycle to design, build, and operate systems that are reliable by default.
• Long-Term Focus: Leverage AI-driven automation to eliminate manual workflows, enabling the team to focus on complex problem-solving and strategic innovation while reducing operational overhead to less than 20% of capacity.

What You'll Actually Be Doing

• Lead incident detection, response, and resolution — driving root cause analysis, postmortems, and systemic fixes to ensure high uptime and rapid recovery.
• Independently drive the design and implementation of automation platforms and self-healing systems using durable workflow engines (Temporal, Airflow, Argo Workflows).
• Architect and build production-grade observability solutions — monitoring, logging, alerting, and tracing — that enable proactive detection and autonomous remediation.
• Design and implement AI/ML-powered operations tools, including anomaly detection, predictive analysis, intelligent runbook automation, and MCP-based operational agents.
• Partner with engineering and product teams to define and uphold SLAs/SLOs, and coach junior engineers through pair programming, design reviews, and code reviews.

You're Our Person If...

• 5+ years of experience in systems and software engineering for large-scale, internet-facing services, with strong knowledge of distributed systems, Linux/Unix internals, and internet architectures (DNS, HTTP, load balancing, caching).
• Proven proficiency in Python and Go (GoLang) with strong software engineering practices (testing, code review, CI/CD).
• Hands-on expertise with containerized architectures (Docker, Kubernetes) and workflow/orchestration engines (Temporal, Airflow, Argo Workflows, or similar).
• Production experience building and operating observability platforms (Grafana, Prometheus, ELK, Splunk, Datadog, or similar).
• Solid background in incident management and SRE principles: on-call, root cause analysis, postmortems, SLIs/SLOs, error budgets, and toil reduction.
• An AI-first approach to engineering, with experience applying AI/ML to operations and using AI tools (e.g., Claude Code, GitHub Copilot, Codex, Cursor) in development workflows.

Even Better If...

• Experience with AI agent frameworks, MCP (Model Context Protocol), or building LLM-powered operational tools.
• Contributions to open-source reliability/observability tooling; experience with chaos engineering and game day exercises.
• AWS/GCP professional-level certifications.
• Prior experience in SRE organizations supporting multi-cloud or hyperscale environments; Python and Go proficiency for systems-level tooling.