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MLOps Engineer

Full-Time MLOps Engineers from $2,000/month

Find vetted MLOps Engineers who fit how your team works in just under 2 weeks, and Kuubiik handles the contract, payments, and HR.

★★★★★ Trusted by 500+ fast-growing companies

How much does it cost to outsource a MLOps Engineer?

Compare monthly outsourcing costs and skill levels across junior, mid-level, and senior MLOps Engineers.

Junior MLOps Engineer

1 - 2 years of experience

Degree in Computer Science, Software Engineering, or a related field
1-2 years of experience in DevOps, data engineering, or ML engineering
Familiarity with CI/CD pipelines and version control (Git)
Basic understanding of containerisation tools (Docker, Kubernetes)
Experience with at least one cloud platform (AWS, GCP, or Azure)
Eager to learn ML lifecycle management tools and best practices

Southeast Asia

$2,000 - $2,800 /mo

LATAM

$3,800 - $5,500 /mo

USA

$8,000 - $10,000 /mo

Mid-Level MLOps Engineer

3 - 5 years of experience

Degree in Computer Science or Engineering
3+ years of experience in MLOps, DevOps, or platform engineering with an ML focus
Hands-on experience with ML platforms (MLflow, Kubeflow, SageMaker, Vertex AI)
Strong Docker and Kubernetes skills for model containerisation and orchestration
Solid Python skills and familiarity with model training and evaluation workflows
Experience building automated training, deployment, and monitoring pipelines

Southeast Asia

$2,800 - $4,200 /mo

LATAM

$5,500 - $7,500 /mo

USA

$12,000 - $15,500 /mo

Senior MLOps Engineer

6+ years of experience

Deep expertise in ML infrastructure, platform engineering, and production ML systems
6+ years of experience managing ML lifecycle at scale in production environments
Proven ability to design end-to-end MLOps architectures and platform strategies
Expert-level knowledge of model serving, monitoring, and drift detection systems
Experience leading MLOps teams and defining engineering standards
Track record of reducing model deployment time and improving system reliability

Southeast Asia

$4,200 - $6,000 /mo

LATAM

$7,500 - $11,000 /mo

USA

$15,500 - $20,000 /mo

MLOps Engineer responsibilities and core areas of work

ML Pipeline Development

An MLOps Engineer designs and builds end-to-end ML pipelines covering data ingestion, training, evaluation, and deployment.
Automate pipeline execution and ensure pipelines are reproducible and version-controlled.
Integrate pipelines with CI/CD systems so models are tested and deployed with the same rigour as application code.

Model Deployment & Serving

Package models as containerised services and deploy them to cloud-native inference platforms.
Set up model serving infrastructure with low-latency endpoints for real-time and batch inference.
Manage model versioning and canary deployments to enable safe, gradual rollouts.

Monitoring & Observability

Implement monitoring systems that track model performance, data drift, and prediction quality in production.
Set up alerting workflows so teams are notified immediately when a model degrades.
Build observability dashboards that give data scientists and stakeholders visibility into model health.

Experiment Tracking

Set up experiment tracking platforms (MLflow, Weights & Biases, Neptune) to log runs, metrics, and artefacts.
Enforce consistent experiment logging practices across the data science team.
Maintain model registries that document approved models and their associated metadata.

Infrastructure & Cloud

Provision and manage ML compute resources on cloud platforms (AWS, GCP, Azure) efficiently.
Configure Kubernetes clusters and GPU workloads for scalable model training.
Optimise infrastructure costs by implementing spot instances, autoscaling, and resource quotas.

Retraining & Continuous Learning

Build automated retraining pipelines triggered by data drift, scheduled intervals, or performance thresholds.
Validate newly trained models against holdout datasets before promoting them to production.
Manage the full model lifecycle from initial deployment through deprecation.

Collaboration & Enablement

An MLOps Engineer works closely with data scientists to operationalise their research models into production-grade systems.
Provide tooling and documentation that accelerates the ML development cycle.
Advocate for MLOps best practices within the engineering organisation and drive adoption.

Finding the right resource has never been more flexible

What is MLOps?

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MLOps combines DevOps practices with machine learning...

What tools do you use?

We use Kubernetes, Docker, and CI/CD pipelines...

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Full-Time MLOps Engineers from $2,000/month

★★★★★ Trusted by 500+ fast-growing companies

How much does it cost to outsource a MLOps Engineer?

Junior MLOps Engineer

Mid-Level MLOps Engineer

Senior MLOps Engineer

MLOps Engineer responsibilities and core areas of work

ML Pipeline Development

Model Deployment & Serving

Monitoring & Observability

Experiment Tracking

Infrastructure & Cloud

Retraining & Continuous Learning

Collaboration & Enablement

Finding the right resource has never been more flexible

What is MLOps?

What tools do you use?

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SALES

MARKETING

CREATIVE

Administrative Assistant

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