DevOps Built for the Age of AI

AI applications require a different infrastructure stack. LLMOps, model serving, GPU-aware pipelines, and experiment tracking — we build the DevOps foundation that Qatar's AI teams need.

Duration: 4-12 weeks Team: 1 AI DevOps Architect + 1 MLOps Engineer

The Challenge

You might be experiencing...

Your data science team produces models that never make it to production — the gap between Jupyter and a production API is a 6-month engineering project.

You're serving an LLM in production but have no observability — you don't know latency, cost per request, or drift.

Your AI application's infrastructure costs are unpredictable — GPU instances running 24/7 for workloads that run for 2 hours per day.

You need to A/B test two model versions in production but have no infrastructure for traffic splitting between model versions.

AI-native DevOps bridges the gap between AI research and AI production. As Qatar’s engineering teams build more AI-powered products — from QCRI research initiatives to QatarEnergy digital twin programs to commercial AI applications in Doha’s growing tech sector — the infrastructure underneath them requires specialist knowledge that traditional DevOps engineers don’t always have.

Qatar’s AI Ecosystem

Qatar is investing heavily in AI research and commercialisation. QCRI (Qatar Computing Research Institute) drives foundational AI research, Qatar Foundation supports AI education and innovation, and QatarEnergy is deploying digital twins and predictive analytics across the LNG supply chain. Commercial AI companies licensed under QFC are building Arabic language models, fintech AI, and energy sector predictive platforms.

All of these initiatives share a common infrastructure need: LLMOps pipelines in Qatar that take models from experiment to production reliably, cost-efficiently, and with full observability. The gap between a working Jupyter notebook and a production-grade AI application serving real users is where most AI projects stall.

Our AI DevOps Approach

We build the infrastructure layer that AI teams need: model serving with GPU-aware Kubernetes scheduling, experiment tracking that makes ML work reproducible, and LLM observability that gives you real-time visibility into token costs, latency, and model quality. GCP’s me-west1 region in Doha provides local GPU access for latency-sensitive inference workloads, while AWS Bahrain offers broader service integration.

AI-native DevOps in Qatar also means understanding data residency implications — Qatar NCA requirements affect where training data can be stored and processed, which in turn constrains your choice of GPU cloud region and model serving architecture.

Our Approach

Engagement Phases

Week 1-2

AI Infrastructure Audit

Assess current AI/ML infrastructure: how models are trained, versioned, deployed, and monitored. Identify the gap between experiment and production. Map GPU resource utilisation and cost.

Weeks 3-6

MLOps Pipeline

Implement ML pipeline: data versioning (DVC), experiment tracking (MLflow or W&B), model registry, and automated retraining triggers. Configure reproducible training environments with container-based jobs.

Weeks 7-10

Model Serving Infrastructure

Deploy model serving: vLLM or TGI for LLMs, Triton Inference Server for classical ML. Configure GPU-aware Kubernetes scheduling. Implement A/B testing and canary model deployments.

Weeks 11-12

LLMOps & Observability

Implement LLM-specific observability: token cost tracking, latency percentiles, prompt/response logging (with PII redaction), and model drift detection. Configure alerts for degraded model quality.

What You Get

Deliverables

AI infrastructure architecture diagram

MLOps pipeline (training → evaluation → registry → production)

Model serving infrastructure (GPU-aware Kubernetes)

Experiment tracking setup (MLflow or Weights & Biases)

LLM observability dashboard (cost, latency, quality metrics)

A/B testing infrastructure for model versions

GPU resource optimisation (spot instances, auto-scaling)

Expected Outcomes

Before & After

Metric	Before	After
Model Time to Production	3-6 months: manual handoff from data science to engineering	1-2 weeks: automated pipeline from training to serving
GPU Cost	24/7 GPU instances for batch workloads	50-70% cost reduction via spot instances and auto-scaling
AI Production Visibility	No observability — flying blind on model performance	Full visibility: cost, latency, quality, and drift alerts

Technology

Tools We Use

MLflow / Weights & Biases vLLM / TGI / Triton NVIDIA GPU Operator / KEDA DVC LangSmith / Phoenix

Common Questions

Frequently Asked Questions

What is LLMOps?

LLMOps (Large Language Model Operations) is the set of practices for deploying, monitoring, and maintaining LLM-based applications in production. It extends MLOps with LLM-specific concerns: prompt versioning and evaluation, token cost management, context window optimisation, RAG pipeline observability, and safety monitoring. As LLMs become a core part of Qatar's AI-driven engineering products, LLMOps is becoming as essential as standard DevOps.

Do we need GPU servers on-premise or can we use cloud GPUs?

For most Qatar companies, cloud GPUs (AWS p3/p4/g5 in Bahrain, Azure NCsv3 in UAE North, GCP A100s in me-west1 Doha) are the right answer — they offer flexibility, no capital expense, and spot pricing for training workloads. On-premise GPUs make sense when: you have very high and predictable GPU utilisation (> 60%), you have strict data sovereignty requirements under Qatar NCA, or you're running at a scale where reserved GPU capacity is cost-effective.

How do we evaluate LLM quality in production?

LLM quality evaluation in production uses a combination of: automated metrics (BLEU, ROUGE, BERTScore for summarisation tasks; exact match for structured outputs), LLM-as-judge (using a reference model to score outputs), human feedback collection via thumbs up/down or rating interfaces, and A/B testing between model versions. We implement the right evaluation approach for your use case — there's no one-size-fits-all LLM metric.

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