Hire MLOps engineer

Hire MLOps engineer in 48 hrs — pre-vetted, risk-free.
Our Unique Selling Point is SmartMatch™: an AI-driven vetting engine that filters the top 2 % of global talent. Average hiring time is just 4.6 days from brief to first commit.

CVs in 24 hours
Senior-level code & infra tests
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Hire the best

Why outstaff instead of hire in-house?
When you Hire MLOps engineer through smartbrain.io’s augmentation model, you unlock instant access to niche expertise without the sunk cost of full-time payroll, recruitment fees, or lengthy notice periods. Our specialists arrive production-ready, bringing proven CI/CD for ML, model monitoring, and cloud-native pipelines. You scale your team up or down in days, not quarters, while we shoulder HR, hardware, continuous training, and retention. The result: predictable OPEX, faster releases, uncompromised IP security, and the freedom to invest capital where it matters — core innovation, not headcount management.

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Reviews

We received three senior MLOps profiles within 36 hours. The engineer we chose automated our SageMaker pipelines, integrated model drift monitoring, and built a reproducible CI/CD workflow. Time-to-release dropped by 40 %. smartbrain.io handled contracts, hardware, and onboarding, letting my team focus on feature engineering instead of DevOps firefighting.

Linda Moreno

CTO

FinSight Analytics

The onboarding speed was impressive. smartbrain.io delivered an MLOps DevOps specialist who containerized our TensorFlow models and set up Kubeflow in under a week. Build failures fell by 70 %, and we avoided six months of hiring overhead.

Marcus O'Neal

VP Engineering

HealthTrack Systems

Our retail demand-forecasting project stalled until smartbrain.io stepped in. Their remote MLOps engineer rebuilt our data pipelines on GCP, enabling continuous training and blue/green model rollout. Sales forecast accuracy improved by 9 % within the first quarter.

Emily Harper

Head of Data Science

ShopWave Retail

Regulated environments need bullet-proof compliance. The outstaffed engineer ensured HIPAA-grade security, automated audits, and delivered MLflow governance dashboards. smartbrain.io’s flexible contracts meant zero long-term liability while we validated our healthcare MVP.

Derrick Boyd

Chief Information Security Officer

MediCore Labs

Latency in our fraud-detection models was killing conversion. An expert from smartbrain.io refactored our feature store and introduced serverless model serving on AWS Lambda. Checkout latency shrank by 38 % and charge-backs fell accordingly.

Sofia Reynolds

Engineering Manager

TrustPay Fintech

We needed true 24/7 coverage for anomaly detection. smartbrain.io supplied two time-zone staggered MLOps consultants who implemented Prometheus-based monitoring and auto-healing Kubernetes clusters. Mean-time-to-recovery is now under 90 seconds.

Thomas Chen

Operations Director

GridSense Energy

Industries

FinTech Risk Scoring

FinTech firms rely on real-time model updates to flag fraud and price risk. Outstaffed Hire MLOps engineer talent from smartbrain.io builds PCI-compliant CI/CD pipelines, manages feature stores, and delivers zero-downtime Canary deployments. Faster release cycles mean lower false-positive rates and happier customers.

Healthcare Diagnostics

Digital health platforms must meet HIPAA while shipping AI diagnostics. Our outstaffed MLOps experts automate PHI encryption, DICOM ingestion, and model version control, letting clinicians focus on patient outcomes instead of infrastructure headaches.

eCommerce Personalization

Retail & eCommerce brands use MLOps to recommend products in milliseconds. smartbrain.io’s outstaffed engineers optimize latency via serverless inference, A/B testing rigs, and auto-scaling GPU nodes—boosting average order value without CAPEX spikes.

Autonomous Mobility

Automotive & Mobility companies generate petabytes of sensor data daily. Our contractors orchestrate distributed training, continuous evaluation, and over-the-air model updates that keep fleets safer and regulatory audits smoother.

Energy Forecasting

Utilities need accurate load predictions. Outstaffed MLOps talent implements time-series feature pipelines, on-edge model deployment, and robust monitoring—cutting forecast error and helping grids stay green.

AdTech Optimization

Advertising platforms win on bid-time speed. Our engineers deliver ultra-low-latency inference graphs and streaming data validation, increasing ROI across campaigns.

InsurTech Underwriting

Insurers modernize underwriting via ML. smartbrain.io supplies MLOps developers who containerize actuarial models, enforce SOC 2, and automate retraining on new claims data.

Logistics Routing

Supply-chain players count on live ETA predictions. Outstaffed experts optimize data lakes, Kafka streams, and high-availability deployment clusters—cutting delivery delays.

Media Streaming

OTT providers personalize content via recommendation engines. Our augmentation service automates model rollbacks and ABR bitrate tuning, reducing churn.

Case Studies

Client: Series-B online payments company.
Challenge: They had to Hire MLOps engineer fast to rebuild a brittle fraud-detection pipeline suffering 800 ms latency.

Solution: smartbrain.io placed two pre-vetted MLOps specialists in four days. The augmented team migrated models to Docker, set up GitOps, and implemented Kafka-based feature streaming.

Result: In six weeks, transaction scoring latency fell by 63 %, false positives dropped 18 %, and the startup avoided a projected $1.2 M recruitment spend.

Client: Nationwide telemedicine provider.
Challenge: Urgent need to Hire MLOps engineer able to meet HIPAA logging mandates during a pandemic usage spike.

Solution: A smartbrain.io engineer integrated MLflow with encrypted S3 buckets and Terraformed audit-ready infrastructure, working under a flexible outstaffing agreement.

Result: Compliance audit passed with 0 findings, video-diagnostics models retrain nightly, and physician wait-time was cut by 22 %.

Client: Fortune-500 equipment maker.
Challenge: Need to Hire MLOps engineer capable of streaming vibration data from 2,000 machines.

Solution: Our outstaffed duo deployed Edge-to-Cloud ingestion, set up Airflow DAGs, and introduced Canary deployments for LSTM anomaly detectors.

Result: Unplanned downtime reduced by 48 % and maintenance cost savings exceeded $4.7 M annually.

Book Your 15-Min Discovery Call

120+ MLOps engineers placed, 4.9/5 avg rating. Partner with smartbrain.io’s on-demand talent pool to launch, scale, or rescue ML pipelines without recruitment bottlenecks.

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Core Skill Groups

Cloud & Infrastructure Automation

Spin-up ready-to-scale infrastructure across AWS, Azure, and GCP in hours. Outstaffed engineers master Terraform, CloudFormation, and Kubernetes, ensuring reproducible environments for every experiment. Business value: launch new ML services without CapEx or vendor lock-in while maintaining SOC 2 and GDPR compliance.

CI/CD & IaC for ML

Continuous delivery for models is different from apps. Our contractors build GitOps workflows, automate model testing, and trigger blue/green rollouts with Helm. Result: fewer rollbacks, faster iteration, and measurable MTTD/MTTR improvements.

Model Deployment & Serving

From notebook to production via Docker, KFServing, SageMaker, or TorchServe. Outstaffed talent selects optimal serving patterns—batch, real-time, or streaming—cutting inference costs and slashing latency.

Monitoring & Observability

Know when models break. Engineers instrument Prometheus, Grafana, DataDog, and custom drift detection, delivering dashboards executives actually read. Faster incident response protects revenue and reputation.

Data Engineering Pipelines

Clean data equals better models. Specialists architect Airflow DAGs, Spark jobs, and Delta Lake schemas that feed features continuously—unlocking fresh insights without manual intervention.

Security & Compliance

Stay audit-ready. MLOps engineers embed IAM, Secret Management, and policy-as-code, ensuring every pipeline change is logged, encrypted, and reversible—critical for regulated verticals.

FAQ

What is the real cost to hire MLOps developers through smartbrain.io?

The headline rate you see on a candidate’s profile is the all-inclusive cost—no sourcing fee, payroll tax, or healthcare burden on your side. When you Hire MLOps engineer via our outstaffing model you pay a single monthly invoice that covers salary, local benefits, hardware, and continuous up-skilling. Typical savings versus U.S. direct hiring range from 30 % to 55 %, and you avoid the 8–12 weeks of recruiter retainer fees entirely. Because contracts are month-to-month, you can scale your burn rate up or down with product needs instead of payroll cycles.

How do you protect our IP when augmenting MLOps teams offshore?

Every engineer signs a tri-party NDA covering client, smartbrain.io, and talent before any code is written. Source control lives in your repositories, access keys rotate automatically, and we enforce zero-trust VPN plus SSO. Our SOC 2 Type II audit trail provides the same or higher level of intellectual-property protection you would create in-house, without the overhead. We also offer jurisdiction-specific contracts to align with your legal team’s requirements.

Will time-zone differences slow down collaboration with an outstaffed MLOps consultant?

No. We maintain talent hubs across North America, LatAm, and EMEA, enabling 4-hour minimum overlap with any U.S. time zone. Sprint ceremonies, stand-ups, and incident response remain synchronous, while off-hour tasks like nightly retraining benefit from true 24/7 coverage.

How fast can we start once we sign the Master Service Agreement?

Our average lead time is 48 hours for the first vetted shortlist and 4-6 days until the engineer’s first commit. Because talent is pre-interviewed, you’re skipping phone screens and coding tests. The process is faster than relocating an internal employee from another department.

What contract terms and exit clauses are typical?

All engagements are month-to-month with a simple 15-day notice period. No lock-ins, no penalties, and you retain the option to convert an engineer to full-time employment after six months for a modest success fee.

How rigorous is your candidate vetting for senior MLOps engineers?

Each applicant completes a five-stage funnel: algorithmic coding test, architecture interview, live Kubernetes exercise, soft-skills assessment, and reference checks. Only the top 2 % reach our client bench, ensuring you meet genuine senior talent rather than CV keywords.

What happens if the assigned MLOps engineer is not a good fit?

Every contract includes a two-week risk-free replacement guarantee. If performance, culture, or domain fit misses expectations, we provide a replacement within five working days at no cost while covering the knowledge transfer overhead ourselves.

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