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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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Jj Huertas

Jj Huertas

Mid-level
Freelance, Full Time
chatgpt ai jobs
ai workflow integration full-stack development devops & containerization database design & optimization api engineering +5
Open profile
Priyanshu Dey

Priyanshu Dey

Mid-level
Full Time, Part Time
AI & Machine Learning Engineers NLP & Prompt Engineering
python gcp langchain rag hugging face +5
Open profile
Yassin Salimu

Yassin Salimu

Senior-level
Full Time, Part Time
NLP & Prompt Engineering AI & Machine Learning Engineers
python tensorflow pytorch scikit-learn keras +5
Open profile
Yash Singhal

Yash Singhal

Entry-level
Contract, Internship
MLOps & Industrialisation NLP & Prompt Engineering
reinforcement learning langchain rag mistral aws +4
Open profile
Arya Sarkar

Arya Sarkar

Mid-level
Contract, Part Time
Research ml (machine learning)
python php llm aws deep learning +3
Open profile
Frdin Derekeh

Frdin Derekeh

Mid-level
Freelance, Contract
data science deep learning
python sql git numpy pandas +4
Open profile
Shyam Kumar V N

Shyam Kumar V N

Senior-level
Full Time
Big Data data science
python mlops llmops data science apache spark +3
Open profile
Samuel Lukudu

Samuel Lukudu

Mid-level
Full Time, Contract
deep learning prompt engineering
python pytorch tensorflow numpy huggingface +5
Open profile
Gaurav Gaikwad

Gaurav Gaikwad

Entry-level
Full Time
data science generative ai
python machine learning deep learning rag nlp +4
Open profile
M Bisyri Mustofa

M Bisyri Mustofa

Entry-level
Freelance, Part Time
computer vision data science
sql server python tensorflow pytorch opencv +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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