Fakhar A.Private profile: Upwork shows its earnings only to signed-in members, so we check it every few weeks, not daily. MRR here is a rough estimate (Upwork's six-month earnings ÷ months active). Last read Nov 5, 2026.

I build and operate the platforms that put AI into production — and keep it there. DevOps, MLOps, SRE, and AIOps for teams running serious workloads on AWS, Azure, and Google Cloud, with deep hands-on expertise in Kubernetes, GPUs, and LLM serving. If you're shipping an LLM product, moving training pipelines onto Kubernetes, taming a noisy multi-cloud estate with real SRE practices, or trying to stop burning money on idle GPUs — that's exactly where I plug in. 🚀 AI/ML Platforms on Kubernetes + GPUs Production LLM serving with vLLM, NVIDIA Triton, TGI, and KServe · distributed training on KubeRay, Kubeflow, and Volcano · GPU cost optimization with the NVIDIA GPU Operator, MIG partitioning, time-slicing, and Karpenter · RAG platforms on pgvector, Weaviate, Milvus, and Pinecone · quantization (AWQ, GPTQ, FP8) and continuous batching for high-throughput inference. ⚙️ MLOps & LLMOps Pipelines End-to-end pipelines with Kubeflow, MLflow, Metaflow, Dagster, and Airflow · model registries with staged rollouts and canary evaluation · LLM observability with LangSmith, Langfuse, Arize Phoenix, and Ragas · feature stores with Feast and Tecton · data quality with Great Expectations, dbt, Delta Lake, and Iceberg. 📈 Site Reliability Engineering (SRE) SLO / SLI / error-budget frameworks tuned for both classical services and AI workloads (TTFT, tokens/sec, drift) · unified observability with OpenTelemetry, Prometheus, Grafana (Loki, Tempo, Mimir), Datadog, and Dynatrace · chaos engineering with Chaos Mesh, LitmusChaos, and AWS FIS · incident management with PagerDuty and Rootly · blameless postmortems that actually change the system. 🤖 AIOps & Intelligent Automation AI-driven anomaly detection and alert correlation with Datadog Watchdog, Dynatrace Davis AI, New Relic AI, and Elastic ML · LLM-assisted incident triage and root-cause summarization · self-healing runbooks with Ansible, StackStorm, and Rundeck · AI-powered FinOps with CloudZero, Kubecost, and native cloud cost anomaly detection. 🏗️ Cloud & Kubernetes Architecture (AWS · Azure · GCP) Production Kubernetes on EKS, AKS, and GKE · multi-region, HA, and DR designs · IaC with Terraform, OpenTofu, Terragrunt, and Crossplane · GitOps with Argo CD and Flux · CI/CD with GitHub Actions, GitLab CI, Azure DevOps, Jenkins, and AWS CodePipeline · progressive delivery with Argo Rollouts and Flagger · internal developer platforms with Backstage and Port. 🔒 Cloud Security & DevSecOps Zero-trust IAM across AWS IAM Identity Center, Entra ID, and Google Cloud IAM · shift-left security with Trivy, Snyk, Semgrep, Wiz, and Aqua · policy-as-code with OPA/Gatekeeper and Kyverno · supply-chain security with Sigstore/Cosign and SBOMs · AI/ML governance aligned to NIST AI RMF, ISO/IEC 42001, and the EU AI Act · SOC 2, ISO 27001, HIPAA, PCI-DSS, and GDPR compliance. RECENT OUTCOMES I'VE DELIVERED FOR CLIENTS (Fill in with your real numbers — clients respond to specificity) Reduced LLM inference cost per 1M tokens by 35% through vLLM tuning, MIG partitioning, and Karpenter GPU autoscaling Cut production MTTR by 50% with SLO-driven alerting, OpenTelemetry, and AI-assisted triage Migrated multiple training workloads to a fully GitOps-managed Kubeflow platform, shrinking release cycles from weeks to hours Built multi-region, [HIPAA / SOC 2]-compliant AI platforms on [AWS / Azure] from zero to production in 6 weeks Saved $900/month on GPU spend through rightsizing, spot/preemptible strategies, and idle-node reclamation HOW WE'LL WORK TOGETHER Discovery call — I learn your architecture, constraints, and what "success" actually looks like. Written proposal — scope, milestones, timeline, and pricing agreed before any work starts. Delivery — async-friendly updates, clean IaC, real documentation, and knowledge transfer so your team owns the outcome. Handover & optional retainer — runbooks, dashboards, and ongoing SRE / AIOps support if you want it. LET'S TALK IF YOU NEED: ✔ An AI platform architected end-to-end on Kubernetes + GPUs ✔ GPU costs brought under control on training or inference ✔ MLOps or LLMOps pipelines built, hardened, or migrated ✔ SRE, SLOs, and real observability introduced to a growing team ✔ AIOps layered onto an existing noisy multi-cloud stack ✔ A hands-on architect for cloud migration, modernization, or platform engineering Send me a short description of your project or the problem you're stuck on — I usually reply within a few hours with initial thoughts, questions, and a suggested next step.