AI-Augmented Site Reliability Engineer

If it's down, it's on you. If it stays up, that's on you too.

This isn't a ticket-queue DevOps gig where you spin up infra and wait for the next request. At Techdome, you own production — for real Healthcare, FinTech, AI, and SaaS products, with real users depending on uptime that isn't negotiable.

You'll build the pipelines, own the incidents, ship the zero-downtime releases, and be the person the team trusts when something breaks at 2am. If automation, Kubernetes, observability, and AI-powered ops genuinely excite you — not just as buzzwords on a resume — keep reading.

What You'll Actually Do

• Keep production up, fast, and stable — availability, reliability, scalability, and performance across every environment you touch.
• Run the cloud like it's yours — manage and optimize environments across AWS, Azure, or GCP.
• Build deployment pipelines that don't break things — CI/CD engineered for zero-downtime, with Blue-Green, Rolling, and Canary strategies as your default toolkit, not a slide in a deck.
• Codify the infrastructure — Terraform and Ansible, so nothing important lives only in someone's head.
• See problems before they become incidents — observability with Prometheus, Grafana, ELK, Datadog, OpenTelemetry, and centralized logging.
• Own the numbers that matter — define and maintain SLIs, SLOs, and error budgets, not just watch dashboards.
• Lead when things go wrong — incident management, RCA, and post-incident reviews that actually prevent the repeat.
• Watch the bill, not just the uptime — cloud cost optimization and capacity planning.
• Automate the boring and the risky — scripting and AI-powered tooling for alert triage, incident summarization, and operational workflows.
• Carry the pager — join the on-call rotation, because ownership doesn't stop at 6pm.
  
  

• 3+ years as an SRE, DevOps Engineer, Platform Engineer, or Cloud Engineer.
• Real hands-on cloud experience — AWS, Azure, or GCP.
• Docker and Kubernetes fluency — not "I've used it once," but production experience under real load.
• Infrastructure-as-Code expertise — Terraform, Ansible, or equivalent.
• CI/CD pipelines built from scratch — Jenkins, GitHub Actions, GitLab CI, or similar.
• Solid Linux, networking, and distributed-systems fundamentals — the stuff that doesn't show up in a tutorial.
• Scripting chops — Python, Go, or Bash.
• Experience running large-scale production environments where "it's fine" isn't good enough.
• Working knowledge of Blue-Green, Canary, and Rolling deployments — in practice, not just in theory.
• Time in FinTech, Payments, Healthcare, or another high-availability environment where downtime has real consequences.
• A working AI toolkit — Copilot, Claude, Cursor, ChatGPT, or similar — used to actually move faster, not just talk about it.
  
  

• You've built AI-powered operational workflows — monitoring, alert triage, incident summarization, automation — not just used AI to write scripts faster.
• You speak fluent SRE: SLOs, SLIs, error budgets, chaos engineering, and reliability engineering are habits you practice, not terms you've heard.
  
  

• No narrow lane — work across real-world AI, Healthcare, Payments, and SaaS products, not one domain for years on end.
• Ownership from day one — critical production infrastructure is yours, not something you inherit after two years of proving yourself.
• Scale that matters — systems supporting thousands of users and business-critical workflows, not internal tools nobody depends on.
• Direct access to the top — work with founders and senior engineering leadership, no six layers of management between your idea and a decision.
• An AI-first culture that's actually real — modern tooling and real automation, not a mandate to "use AI more" with no support behind it.
• Speed and real ownership — fast decisions, real stakes, and growth that isn't stuck waiting for a title change.