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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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AI & ML Engineering Data Science & Analytics NLP & Prompt Engineering MLOps & AI Infrastructure Automation & AI Workflows

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Tory Mascuilli

Tory Mascuilli

Mid-level
Full Time, Freelance
Research data science
r sql tableau statistical modeling python +3
Open profile
Kent Tommy Danielsson

Kent Tommy Danielsson

Senior-level
Full Time
Product AI Product & Strategy
python mlops devops llm vector databases +3
Open profile
Sai Arthi R

Sai Arthi R

Mid-level
Part Time, Full Time
MLOps ml (machine learning)
git python pytorch c++
Open profile
Priyanka Velaga

Priyanka Velaga

Mid-level
Full Time, Part Time
MLOps & Industrialisation NLP & Prompt Engineering
tensorflow pytorch sql docker kubernetes +5
Open profile
Alex Carlier

Alex Carlier

Senior-level
Freelance, Full Time
computer vision ml (machine learning)
python pytorch api aws cuda +5
Open profile
Isaac Menchaca

Isaac Menchaca

Mid-level
Full Time, Contract
ml (machine learning) MLOps
python llm tensorflow mlops aws sagemaker +5
Open profile
Bhuwan Bhandari

Bhuwan Bhandari

Entry-level
Full Time, Internship
ml (machine learning) AI & Machine Learning Engineers
python ai/ml posgresql redis aws +5
Open profile
Sarthak Shilwant

Sarthak Shilwant

Entry-level
Full Time, Part Time
prompt engineering generative ai
python machine learning deep learning natural language processing (nlp) generative ai +4
Open profile
Fahad Hassan

Fahad Hassan

Senior-level
Contract, Full Time
data science ml (machine learning)
python machine learning sql data science ai +1
Open profile
Hammad Ahmed

Hammad Ahmed

Entry-level
Full Time, Internship
AI & Machine Learning Engineers MLOps & Industrialisation
python tensorflow pytorch scikit-learn keras +5
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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