Senior DevOps Engineer

Nayatel; Islamabad; Presencial; PKR 1.800.000 - 3.200.000; Jornada completa

Descripción de la vacante

Nayatel is seeking an engineer to join the AI Platform team responsible for building and operating the cloud infrastructure, LLM gateway, and serving layer behind our wealth management product. The role is hands-on and centers on taking infrastructure from code to production on Azure, ensuring reliability and cost-effectiveness.

You will own critical tooling and pipelines, collaborating with data scientists and product teams to scale AI-driven services with a strong emphasis on observability,

Formación

• 3-5 years of platform/backend/infrastructure engineering with production ownership.
• Hands-on cloud experience with Azure/AWS/GCP or equivalent, including core compute and networking.
• Infrastructure as code with Terraform, Bicep, or comparable tool; modules and state management.
• Strong debugging instincts using logs, traces, and reproduction steps.
• Working knowledge of LLMs: tokens, context windows, quotas and constraints on builds.
• Fluency with AI-assisted development tools like Claude Code, Cursor, OpenCode, or similar.

Conocimientos

Platform/Backend ownership

Cloud platforms

LLM concepts

Python asyncio

Debugging

Observability

Cost optimization

AI tool familiarity

System design

Proxy/gateway concepts

Herramientas

Terraform

Bicep

OpenTelemetry

App Insights

LLM gateway/proxy

vLLM/Ollama/TGI

Descripción del empleo

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)