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Senior DevOps Engineer

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Onsite

Senior DevOps Engineer

Nayatel

Senior DevOps Engineer

Skills

Google Cloud PlatformPythonAIAWSBudgetingTerraformAzure

8 days ago

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Important information


Contract type:

Freelance / Contract

Location:

Islamabad, Islamabad Capital Territory, Pakistan

Work mode:

Onsite

Published on:

13 October 2026

What they need


Job Overview:

You’ll join the AI Platform team building and operating the cloud infrastructure, LLM gateway, and

serving layer behind our wealth management product. This is a hands-on ownership role on Azure for an engineer who can take infrastructure from code to production and keep it running reliably and cost-effectively.

Requirements:

  • 3–5 years of platform / backend / infrastructure engineering, with real ownership of things running in production.
  • Hands-on cloud experience with any major provider — Azure, AWS, GCP, or equivalent —including a solid grasp of core compute and networking (private networking, container/serverless compute, and how traffic flows between services).
  • Infrastructure as code with Terraform, Bicep, or a comparable tool — modules, state, and the discipline of managing infra in code.
  • Solid debugging instincts — you reach for logs, traces, and reproduction steps before guessing.
  • Working knowledge of LLMs: what tokens, context windows, and quotas mean in practice, and how those constraints shape what you can build.
  • Fluency with modern AI-assisted development tools — Claude Code, Cursor, Antigravity, OpenCode, or similar.

Nice to Have:

  • Hands-on Azure specifically — VNets, subnets, private endpoints, Container Apps, Function Apps.
  • System design experience, familiarity with AI infrastructure terms (tokenization, requests per minute, token budgeting)
  • Strong Python, including async (asyncio), and a working grasp of concurrency, threading vs.async, and the pitfalls of each.
  • Experience building or operating an LLM gateway or similar proxy/routing layer.
  • Open-source model deployment and serving — running and hosting your own models (vLLM, Ollama, TGI, or similar), and the infra/optimization that comes with it.
  • Exposure to LLM safety and governance — PII redaction, data classification, prompt/response guardrails.
  • Cost optimization across cloud compute (plans, scaling, cold starts on serverless).
  • Observability tooling (App Insights, OpenTelemetry, or similar)

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