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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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Kamlesh Batham

Kamlesh Batham

Mid-level
Full Time
AI & Machine Learning Engineers data science & analytics
python spark
Open profile
Sanan Mehdiyev

Sanan Mehdiyev

Mid-level
MLOps ml (machine learning)
python docker machine learning ci\cd kubernetes +5
Open profile
D

Drew Van Deventer

Senior-level
Full Time, Part Time
AI & Machine Learning Engineers NLP & Prompt Engineering
python tensorflow pytorch computer vision kafka +5
Open profile
F

Francis Batista

Senior-level
Full Time, Part Time
generative ai ml (machine learning)
python genai sql mongodb nodejs
Open profile
S

Subrata Mondal

Entry-level
Full Time, Internship
ml (machine learning) MLOps
python sql machine learning deep learning docker +2
Open profile
E

Eddie Otudor

Senior-level
Full Time, Part Time
prompt engineering deep learning
python pytorch deep learning nlp transformers architecture +1
Open profile
T

Taj Khunkhun

Senior-level
Full Time, Contract
AI & Machine Learning Engineers
tensorflow keras databricks etl pipelines power bi +4
Open profile
H

Hamza

Mid-level
Full Time, Contract
ai jobs ml (machine learning)
python sql generative ai (llm) machine learning deep learning +5
Open profile
Joe Sterett

Joe Sterett

Executive-level
Full Time
AI Product & Design AI Business, Marketing & Strategy
notion ai
Open profile
V

Vinodkumar Sargam

Mid-level
Full Time
architecture Big Data
aws mysql mssql linux terraform +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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