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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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S

Shoaib

Senior
Pakistan — Karachi (UTC+5) Full-time, Part-time
AI & Machine Learning Engineers NLP & Prompt Engineering
python tensorflow langchain rag llamaindex +2
Open profile
Lidiia

Lidiia

Mid-Level
Brazil — Boa Vista (UTC-4) Freelance
data science & analytics MLOps & AI Infrastructure
scikit-learn python sql keras
Open profile
Deni Ari

Deni Ari

Junior
Japan — Tokyo (UTC+9) Freelance, Part-time
Automation & AI Workflows AI Product & Strategy
prompt engineering python azure aws
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A

Abdullah

Senior
Netherlands — Amsterdam (UTC+2) Part-time, Full-time
AI Implementation & Consulting AI & Machine Learning Engineers
python sql langchain rag prompt engineering +5
Open profile
I

Indumathi

Mid-Level
India — Kolkata (UTC+5.5) Part-time, Freelance
AI & Machine Learning Engineers Automation & AI Workflows
python sql tensorflow pandas scikit-learn +5
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V

Viraj

Junior
India — Kolkata (UTC+5.5) Full-time, Part-time
data science & analytics MLOps & AI Infrastructure
python scikit-learn tensorflow pandas docker +5
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Bishal Singh

Bishal Singh

Mid-Level
Nepal — Kathmandu (UTC+5.75) Full-time, Part-time
Automation & AI Workflows
python langchain docker kubernetes
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Navaneeth

Navaneeth

Mid-Level
India — Kolkata (UTC+5.5) Part-time, Full-time
AI & Machine Learning Engineers data science & analytics
pytorch python computer vision aws
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Saghar

Saghar

Mid-Level
Iran — Tehran (UTC+3.5) Full-time
AI & Machine Learning Engineers AI Product & Strategy
python llm fine-tuning langchain ai agents pandas +2
Open profile
Shibnath

Shibnath

Junior
India — Kolkata (UTC+5.5) Full-time, Contract-to-hire
Automation & AI Workflows MLOps & AI Infrastructure
python langchain docker kubernetes ci/cd +1
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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