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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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A

Aaron Joseff Pacis

Senior-level
Full Time
MLOps data science
Open profile
Yuliya Masalskaya

Yuliya Masalskaya

Entry-level
Full Time
NLP ml (machine learning)
python tensorflow keras aws azure +1
Open profile
R

Riya

Entry-level
Freelance, Full Time
AI & Machine Learning Engineers NLP & Prompt Engineering
python tensorflow keras numpy prompt engineering +2
Open profile
Anton Dorozhko

Anton Dorozhko

Senior-level
Contract, Freelance
computer vision MLOps
pytorch onnx object detection cuda deepstream +4
Open profile
U

Utkarsh Tiwari

Entry-level
Full Time, Part Time
generative ai MLOps
Open profile
L

Laraib Ahmad Siddiqui

Mid-level
Contract, Full Time
prompt engineering data science
Open profile
Sounak Mukherjee

Sounak Mukherjee

Entry-level
Full Time, Part Time
ml (machine learning) MLOps
python terraform aws tensorflow metaflow +4
Open profile
Raviteja Peri

Raviteja Peri

Senior-level
Full Time
Research generative ai
sql python hcl docker kubernetes
Open profile
J

Jake White

Senior-level
Full Time, Contract
generative ai NLP & Prompt Engineering
Open profile
E

Erin Versfeld

Senior-level
Full Time, Part Time
Engineering AI & Machine Learning Engineers
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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