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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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P

Pooja Kumari

Senior-level
Full Time
generative ai MLOps
python pytorch cuda tensorflow keras +5
Open profile
M

Mhd Saria Al Lahham

Mid-level
Full Time
computer vision Consulting
python java c/c++ pytorch tensorflow +5
Open profile
H

Henry Yan

Senior-level
Part Time
AI & Machine Learning Engineers
python tensorflow pytorch etl pipelines databricks +5
Open profile
P

Pedro Michel Nikolaides

Senior-level
Full Time, Part Time
chatgpt prompt engineering
python langchain fastapi gpt llm +2
Open profile
Y

Yuji Oshima

Senior-level
Full Time, Freelance
MLOps ml (machine learning)
python recommendation system pytorch aws kubernetes +1
Open profile
T

Thanh Nguyen

Senior-level
Part Time, Contract
AI & Machine Learning Engineers NLP & Prompt Engineering
tensorflow pytorch scikit-learn computer vision kafka +5
Open profile
Great

Great

Mid-Level
Nigeria — Lagos (UTC+1) Freelance, Part-time
NLP & Prompt Engineering Automation & AI Workflows
ai agents llm fine-tuning openai api prompt engineering langchain +4
Open profile
R

Roper

Mid-Level
United States — Los Angeles (UTC-7) Full-time, Part-time
AI Business, Marketing & Strategy Automation & AI Workflows
ai agents openai api prompt engineering llm fine-tuning
Open profile
B

Bianca Emi

Junior
Brazil — Sao Paulo (UTC-3) Full-time
AI & Machine Learning Engineers
python databricks docker airflow aws +2
Open profile
Aung Khant

Aung Khant

Mid-Level
Myanmar — Yangon (UTC+6.5) Full-time, Part-time
AI & Machine Learning Engineers NLP & Prompt Engineering
python sql pytorch fastapi
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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