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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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M

Mohammad

Senior
India — Kolkata (UTC+5.5) Contract, Freelance
MLOps & AI Infrastructure Automation & AI Workflows
python aws langchain pytorch rag +5
Open profile
S

Swapnil

Junior
India — Kolkata (UTC+5.5) Full-time
MLOps & AI Infrastructure data science & analytics
python sql pytorch computer vision reinforcement learning +5
Open profile
T

Talha Ishtiaque

Senior
Pakistan — Karachi (UTC+5) Full-time, Part-time
AI & Machine Learning Engineers NLP & Prompt Engineering
python tensorflow pytorch scikit-learn keras +4
Open profile
Morgan

Morgan

Senior
United States — New York (UTC-4) Contract, Freelance
Automation & AI Workflows AI Implementation & Consulting
python sql aws rag azure +2
Open profile
Pradeep Reddy

Pradeep Reddy

Senior
India — Kolkata (UTC+5.5) Full-time, Part-time
MLOps & AI Infrastructure AI & Machine Learning Engineers
python tensorflow pytorch computer vision kubernetes +5
Open profile
J

Jair

Junior
Mexico — Mexico City (UTC-6) Full-time
AI & Machine Learning Engineers
python prompt engineering ai agents docker openai api
Open profile
L

Lembit

Senior
Estonia — Tallinn (UTC+3) Full-time, Part-time
AI & Machine Learning Engineers Automation & AI Workflows
python tensorflow scikit-learn pytorch pandas +5
Open profile
Kevin

Kevin

Mid-Level
Chile — Easter (UTC-5) Full-time, Part-time
AI & Machine Learning Engineers
python pandas langchain ci/cd azure +4
Open profile
S

Shreyansh

Junior
India — Kolkata (UTC+5.5) Full-time, Contract-to-hire
AI Product & Strategy Automation & AI Workflows
python aws openai api ai agents
Open profile
A

Ayush

Junior
India — Kolkata (UTC+5.5) Full-time
AI & Machine Learning Engineers Automation & AI Workflows
python tensorflow docker kubernetes pandas +5
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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