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Add an MLOps engineer to your team — model deployment, Kubernetes, pipeline orchestration, and production observability. Every profile is manually reviewed. Direct contact, no agency.

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Y

Yevhen

Senior
Netherlands — Amsterdam (UTC+2) Full-time
AI & Machine Learning Engineers data science & analytics
python pytorch scikit-learn pandas spark +1
Open profile
J

Janektiruriru

Junior
Poland — Warsaw (UTC+2) Part-time, Full-time
MLOps & AI Infrastructure
mlflow aws azure gcp
Open profile
A

Anirudh

Junior
India — Kolkata (UTC+5.5) Freelance, Contract
AI & Machine Learning Engineers data science & analytics
python tensorflow pytorch reinforcement learning openai api +5
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Muhammad

Muhammad

Junior
Burkina Faso — Ouagadougou (UTC+0) Full-time, Part-time
AI & Machine Learning Engineers data science & analytics
python sql tensorflow pytorch pandas +5
Open profile
B

Bhupin Baral

Senior
Nepal — Kathmandu (UTC+5.75) Full-time, Part-time
AI & Machine Learning Engineers MLOps & AI Infrastructure
python langchain prompt engineering tensorflow pytorch +4
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F

Favour

Senior
Nigeria — Lagos (UTC+1) Full-time, Freelance
MLOps & AI Infrastructure AI Product & Strategy
python computer vision pytorch tensorflow
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Shubham

Shubham

Senior
India — Kolkata (UTC+5.5) Full-time
AI & Machine Learning Engineers MLOps & AI Infrastructure
python sql hugging face kafka
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Even

Even

Junior
Vietnam — Ho Chi Minh (UTC+7) Full-time
MLOps & AI Infrastructure
python pytorch computer vision kafka
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A

Aatif Hussain

Mid-Level
India — Kolkata (UTC+5.5) Full-time, Part-time
AI & Machine Learning Engineers Automation & AI Workflows
python sql openai api aws
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Paolo

Paolo

Junior
Italy — Rome (UTC+2) Part-time, Freelance
Automation & AI Workflows AI & Machine Learning Engineers
ci/cd python docker tensorflow pandas +4
Open profile

Category guide

Find Vetted MLOps Engineers for Your Team

Why hire MLOps & AI Infrastructure experts?

In the era of generative AI, owning a model is no longer enough. The real challenge is deployment, monitoring, reliability, and scale. MLOps and AI infrastructure specialists bring the engineering discipline needed to run production AI systems with Kubernetes, CI/CD pipelines, model versioning, observability, and better GPU cost control.

Verified profiles for critical AI systems

Each MLOps profile on RemoteAI is selected for the ability to build stable, observable, and production-ready platforms. Whether your stack depends on AWS, SageMaker, MLflow, or internal tooling, these professionals help make AI systems dependable over time. If you also want to improve the model experience itself, browse our NLP & Prompt Engineering experts.

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