Hire Remote MLOps Engineers

Hire Senior MLOps Engineers Who Keep Your ML Systems Running in Production

A machine learning model isn’t a product — it’s a math function. The MLOps engineer is the one who turns that function into a reliable, scalable, observable production system that your users can actually interact with. Without MLOps, models live forever in staging, drift silently in production, and consume GPU compute that nobody’s optimizing. With a great MLOps engineer, ML becomes a repeatable business capability.

Our MLOps engineers have built the training pipelines, model serving infrastructure, experiment tracking systems, and monitoring platforms that keep AI and ML systems reliable at Fortune 500 scale — in your time zone, in English, at 50% of US market rates.

What Our MLOps Engineers Build

ML Training Pipelines & Orchestration

Kubeflow Pipelines, Apache Airflow, and Metaflow — our MLOps engineers build reproducible, versioned, scalable training pipelines that run on GPU clusters and return exactly the same model given the same code and data. No more “it worked on my laptop.”

Model Serving Infrastructure

vLLM, Triton Inference Server, TorchServe, BentoML, and Seldon Core — our MLOps engineers build model serving systems that handle auto-scaling, A/B testing, shadow deployments, and canary releases. They manage the GPU infrastructure, batch inference optimization, and cost management that keeps inference affordable at scale.

Model Registry & Experiment Tracking

MLflow, Weights & Biases, and Neptune — our MLOps engineers implement the experiment tracking and model registry infrastructure that makes ML development reproducible and collaborative. Every experiment is tracked, every model is versioned, every deployment is documented.

Feature Stores & Data Pipelines

Feast, Tecton, and custom feature store implementations — our MLOps engineers build the real-time and batch feature serving infrastructure that provides ML models with consistent, fresh features at training time and inference time.

ML Monitoring & Observability

Data drift detection, model performance monitoring, data quality checks, and alerting systems. Our MLOps engineers build the monitoring infrastructure that catches model degradation before it impacts users — with automated retraining triggers that keep models fresh.

MLOps Technology Stack

Orchestration: Kubeflow Pipelines, Apache Airflow, Metaflow, Prefect, ZenML, Kedro

Experiment Tracking: MLflow, Weights & Biases, Neptune, ClearML, Comet

Model Serving: vLLM, Triton Inference Server, TorchServe, TF Serving, BentoML, Seldon Core, Ray Serve

Feature Stores: Feast, Tecton, Hopsworks, custom Redis/Flink implementations

Monitoring: Evidently AI, Arize AI, Whylogs, Great Expectations, custom Prometheus/Grafana stacks

Infrastructure: Kubernetes, Helm, Docker, NVIDIA GPU Operator, Karpenter

Cloud: AWS SageMaker, GCP Vertex AI, Azure ML, Databricks, Ray on any cloud

CI/CD for ML: GitHub Actions, GitLab CI, DVC, CML (Continuous Machine Learning)

Client Success Story: ML Platform Consolidation for a Fortune 500 Retailer

A Fortune 500 retailer had twelve machine learning teams operating with completely independent tooling — each maintaining its own experiment tracking, model registry, feature store, serving infrastructure, and monitoring stack. The result was $4 million in annual redundant cloud infrastructure spend, models taking an average of eight weeks from validation to production deployment, and no organizational visibility into which models were live or how they were performing. Our MLOps engineers designed and deployed a unified internal ML platform using MLflow for experiment tracking and model registry, Kubeflow Pipelines for orchestrated training workflows, Seldon Core for model serving, and Evidently AI for drift monitoring. Time from model validation to production dropped from eight weeks to four days. Annual infrastructure spend decreased by $2.6 million. The platform became the standard for all new ML initiatives across the business.

Client Success Story: LLM Serving Infrastructure for a Generative AI Startup Scaling from 10K to 1M Users

A generative AI startup needed to scale its LLM-powered product from 10,000 to 1 million daily active users in under three months — a growth rate that would have bankrupted the business at its initial per-request inference cost. Our MLOps engineers deployed a vLLM-based serving cluster on Kubernetes with GPU node auto-scaling, implemented continuous batching and KV cache prefix caching to maximize throughput per GPU, and built cost monitoring dashboards that tracked per-request inference cost in real time by model, user tier, and prompt length. GPU utilization jumped from 23% to 81%. Per-thousand-token inference cost dropped 67%. The free tier unit economics became viable for the first time — unlocking the growth flywheel that drove the company’s Series B raise.

Why Companies Choose Our MLOps Engineers

• ML and infrastructure depth: Our MLOps engineers are fluent in both the ML frameworks and the infrastructure layer beneath them — PyTorch model export, ONNX optimization, and Kubernetes GPU scheduling are equally familiar territory
• Cost-conscious by design: GPU compute is expensive. Our MLOps engineers optimize inference throughput, implement intelligent batching, use spot instances for training, and build cost dashboards that give your team visibility into ML infrastructure spend
• Platform thinking: They don’t build one-off solutions — they build ML platforms that make every subsequent model faster to train, easier to deploy, and simpler to monitor
• Security and compliance: ML systems handle sensitive data. Our MLOps engineers build pipelines with proper data lineage, access controls, and audit trails — essential for regulated industries
• 50% cost savings: Fully-burdened rates including salary, benefits, taxes, and HR

Engagement Models

• Individual MLOps Engineer — A senior MLOps engineer embedded in your existing ML or data team. Ideal for standardizing model deployment processes, building a model registry, introducing drift monitoring, or resolving GPU infrastructure bottlenecks that are blocking ML team productivity.
• ML Platform Pods (MLOps + ML engineer + data engineer) — A cross-functional squad covering the full ML platform stack: training infrastructure, feature pipelines, model serving, and monitoring. Common for teams building their first production ML platform from scratch or modernizing an ad hoc collection of scripts and notebooks into a governed, repeatable system.
• Full ML Platform Teams (5–15+ engineers) — Complete MLOps organizations for enterprises where ML infrastructure is a core competency — covering multi-cloud GPU training, LLM serving at scale, feature stores, model governance, and A/B testing infrastructure.
• Contract-to-Hire MLOps Engineers — Evaluate an MLOps engineer’s platform thinking in your actual infrastructure before committing. Engineers who build reusable ML platform components (vs. one-off deployment scripts) reveal their approach immediately.

How To Vet MLOps Engineers

Our MLOps vetting screens for engineers who think in platforms, not pipelines — candidates who build infrastructure that makes every subsequent model easier to deploy. The four-stage process:

1. Technical screening — ML infrastructure fundamentals (Kubernetes GPU scheduling, Kubeflow, MLflow, DVC, Feast or Tecton feature stores), model serving patterns (BentoML, Seldon, Triton Inference Server, vLLM for LLMs), monitoring and drift detection, and cost optimization for GPU workloads. Over 90% of applicants do not pass this stage.
2. Infrastructure design challenge — Design an end-to-end ML platform: automated training pipeline triggered by data drift detection, model evaluation gate with automatic rollback, A/B testing infrastructure for model comparison, and a serving layer optimized for cost-per-inference. Evaluated on platform reusability, observability, and failure mode handling.
3. Live technical interview — Diagnose a GPU utilization problem in a training cluster from logs and metrics, design a serving architecture for an LLM with dynamic LoRA adapter loading, and discuss the trade-offs between different feature store approaches for real-time vs. batch serving.
4. Communication and cross-team screening — MLOps engineers are platform engineers: they serve ML engineers, data scientists, and business stakeholders simultaneously. We assess the ability to build platforms that non-MLOps engineers can actually use, and to communicate infrastructure constraints to ML researchers.

What to Look for When Hiring MLOps Engineers

Senior MLOps engineers have operated ML systems at the point where everything that can go wrong does — training pipeline failures at hour 47 of a 48-hour run, GPU OOM crashes from dynamic batch sizes, silent drift in production models serving millions of predictions daily. Their toolbox is built from these experiences.

What strong candidates demonstrate:

• They design training pipelines as reproducible, resumable units: checkpoint management, artifact versioning, deterministic data loading with fixed seeds, and experiment tracking that makes it possible to reproduce any result from six months ago
• They’ve operated GPU clusters under real load: they know how to diagnose CUDA OOM vs. host memory pressure vs. network I/O bottlenecks, and they’ve tuned NVLink, InfiniBand, and NCCL parameters for multi-GPU training jobs
• They build serving infrastructure with cost-per-inference as a first-class metric: they implement dynamic batching, GPU memory management for concurrent model loading, and intelligent routing between model tiers based on request complexity
• They have a systematic approach to model monitoring: feature distribution drift (KL divergence, Population Stability Index), prediction distribution monitoring, and the business metric correlations that give early warning when a model is degrading before it causes visible business impact

Red flags to watch for:

• Deploying ML models as one-off containers without a model registry — a sign they’ve built deployment solutions rather than platforms, and each new model will require a new one-off process
• No experience with GPU memory optimization — candidates who haven’t tuned batch sizes, mixed precision, gradient checkpointing, or model parallelism may struggle with large model serving
• Treating model monitoring as a post-hoc concern — candidates who add monitoring after deployment rather than designing it into the serving architecture from the start have likely managed systems where problems were discovered by user complaints
• No cost attribution for GPU spend — teams without per-model cost dashboards are spending on ML infrastructure without visibility into which models or experiments are consuming budget

Interview questions that reveal real depth:

• “Walk me through how you’d design a retraining trigger for a recommendation model. What signals would you monitor, what threshold would trigger retraining, and how would you validate the retrained model before routing production traffic to it?”
• “You’re serving a 70B parameter LLM in production with vLLM. GPU utilization is at 95% but throughput is lower than expected. Walk me through your diagnosis process.”
• “How would you design a feature store for a fraud detection system that needs both real-time features (computed at inference time) and batch features (computed nightly)? What are the consistency guarantees you need to maintain?”

Related Services

• Hire AI Developers — The application engineers who build AI products on top of the ML infrastructure our MLOps engineers operate.
• Hire ML Engineers — The model specialists who train and validate the models our MLOps engineers serve in production.
• DevOps & SRE Engineers — Our DevOps engineers handle the non-ML infrastructure — CI/CD, Kubernetes cluster operations, and cloud cost management.
• Data Scientists & Data Engineers — The data professionals who build the pipelines and features that MLOps infrastructure delivers to models.

Want to Hire Remote MLOps Engineers?

We specialize in sourcing, vetting, and placing senior remote MLOps engineers — from individual platform engineers who build reusable training infrastructure and GPU cost dashboards from day one, to complete MLOps organizations building enterprise ML platforms, LLM serving infrastructure with vLLM and Triton, and real-time feature stores at scale. We make it fast, affordable, and low-risk.

Get matched with MLOps engineers →

Ready to hire MLOps engineers who build ML platforms that actually scale — with drift monitoring, reproducible training pipelines, GPU cost visibility, and LLM serving infrastructure? Contact us today and we’ll introduce you to senior MLOps engineers within 48 hours.