Hire MLOps Engineer — models that survive production
Most ML projects die between the notebook and production: no reproducible training, no monitoring, and deploys that break at 2 a.m. An MLOps engineer closes that gap — model deployment pipelines, feature stores, and drift monitoring that keep predictions truly reliable long after launch day.
Senior oversight on every delivery: I'm Omer Muneer Qazi, a Dubai-based Fractional CTO & Solutions Architect with 15+ years of experience and 100+ projects delivered across 6 countries. Need the data foundation first? Hire a data engineer or contact me to start.
MLOps Deliverables, From Training to Production
Model Deployment Pipelines
Containerized model serving on Kubernetes or serverless GPUs with blue-green deploys, autoscaling, and one-click rollback, so new versions ship to production safely with zero downtime, zero drama.
Feature Store & Training Pipelines
Consistent features between training and serving with versioned datasets and reproducible pipelines, eliminating the train-serve skew that silently and steadily degrades model quality once models reach production.
Drift & Performance Monitoring
Continuous tracking for data drift, prediction drift, and business metrics with alert thresholds, so degrading models get flagged and retrained quickly before your users ever notice.
Model Registry & Versioning
Every model version, dataset, and hyperparameter tracked in a registry with lineage, so you can audit, reproduce, or roll back any prediction the system ever made.
CI/CD for ML
Automated pipelines that test, evaluate, and promote models from staging to production, with quality gates and canary releases that block regressions before they reach real user traffic.
Evaluation Harnesses
Offline and online eval suites with golden datasets and LLM-judge or metric-based scoring, so every model change is a measured decision rather than a hopeful guess about quality.
From Notebook to Production, Safely
A structured engagement with no surprises — you’ll always know what’s happening and what’s next.
Model & Infra Audit
I review your models, training code, and infrastructure to find the gaps between notebook experiments and reliable production serving at scale.
Serving Architecture Design
Deployment topology, feature store layout, and monitoring plan agreed up front, sized to your traffic and latency budgets from day one.
Pipeline Implementation
Training, deployment, and monitoring pipelines built iteratively against real workloads, with your first model serving production traffic within weeks of kickoff.
Handover & Runbooks
Monitoring dashboards, retraining playbooks, and rollback procedures your team can run, plus pairing sessions to transfer operational knowledge end to end.
Why Hire Through Omer Muneer Qazi
I’ve taken ML systems to production with teams at Phaedra Solutions, Integriti, Napollo, Nabidios, Nello, and EverestX, across 100+ projects in 6 countries that enabled $25M+ in client revenue. You get someone who has debugged drift at 2 a.m. — not theory, but production judgment.
I’m Dubai-based and work worldwide, covering your time zone for incident response. Note the lane: I build ML deployment and monitoring — pair with a data engineer for warehouse pipelines.
MLOps Engineer FAQs
How is MLOps different from data engineering?
Data engineering builds pipelines and warehouses that move data; MLOps deploys models and keeps them healthy with monitoring, retraining, and safe releases. Most production AI needs both, in that order.
How long until our model is serving production traffic?
A first model behind a monitored endpoint typically ships in three to four weeks. Full CI/CD with feature store, drift monitoring, and retraining loops usually takes eight to twelve weeks.
Do you work with LLM apps or only classical ML?
Both. Classical models get feature stores and batch or real-time serving; LLM apps get eval harnesses, prompt versioning, and cost/latency guardrails. The deployment discipline is the same either way.
What does drift monitoring actually catch?
Input drift when your data changes, prediction drift when outputs shift, and performance drops against business metrics. Alerts fire with enough context to decide: retrain, roll back, or investigate.
Can you take over our existing ML infrastructure?
Yes — I start with an audit of your training and serving setup, stabilize the riskiest parts first, then modernize incrementally. No rip-and-replace unless the current stack is genuinely unsalvageable.
Hire an MLOps Engineer
Tell me about your models, traffic, and latency needs. You’ll get a scoped MLOps plan with timelines and a fixed quote — no vague estimates.