

KPG99 INC
MLOps Engineer
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for an MLOps Engineer, 90% remote, focusing on AWS-based data pipeline and AI/ML model deployment. Requires 3+ years in AWS data engineering, Python, and CI/CD implementation. Contract length and pay rate are unspecified.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
August 18, 2026
🕒 - Duration
Unknown
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🏝️ - Location
Remote
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📄 - Contract
Unknown
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🔒 - Security
Unknown
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📍 - Location detailed
Juno Beach, FL
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🧠 - Skills detailed
#AWS (Amazon Web Services) #ML Ops (Machine Learning Operations) #Automation #DevSecOps #Amazon ECS (Amazon Elastic Container Service) #AI (Artificial Intelligence) #Data Pipeline #Monitoring #EC2 #Security #Terraform #Lambda (AWS Lambda) #ML (Machine Learning) #Deployment #Scala #Data Engineering #Web Services #Redshift #Cloud #Data Ingestion #SageMaker #DevOps #AWS Lambda #Model Deployment #Python #Compliance #S3 (Amazon Simple Storage Service) #Data Science #Infrastructure as Code (IaC)
Role description
Job Title: MLOps Engineer
Location: Remote (Juno Beach, FL)- 90%
Top Skills - Must Haves
Python
Aws
data engineering
machine learning
Top Skills
3+ years of experience working on building and deploying data pipelines in AWS- lambda, S3, ECS
1+ year of experience deploying ML and AI models to AWS - creating the DevOps pipelines to deploy
Experience developing in Python
Summary
We are seeking a highly skilled MLOps Engineer with deep expertise in Amazon Web Services (AWS) to design, build, automate, and maintain machine learning infrastructure and deployment pipelines. This role bridges the gap between Data Science, Software Engineering, and Cloud Operations, ensuring that machine learning models are deployed, monitored, and scaled efficiently in production environments.
The ideal candidate will have experience implementing CI/CD for machine learning, managing cloud-native architectures, containerization, infrastructure as code, and supporting the full machine learning lifecycle using AWS services.
Key Responsibilities
Machine Learning Operations
Design, implement, and maintain scalable MLOps pipelines for model training, testing, deployment, and monitoring.
Automate ML workflows from data ingestion through model deployment and retraining.
Establish model versioning, experiment tracking, and governance processes.
Build and maintain feature stores and model registries.
AWS Cloud Engineering
Develop and manage machine learning infrastructure using AWS services such as:
Amazon SageMaker
AWS Lambda
Amazon ECS/EKS
EC2
S3
Glue
Redshift
CloudWatch
Step Functions
AWS CodePipeline
AWS CodeBuild
CI/CD & Automation
Build CI/CD pipelines for ML applications and data science workflows.
Implement Infrastructure as Code (IaC) using Terraform or AWS CloudFormation.
Automate deployment, testing, rollback, and monitoring processes.
Integrate security and compliance controls into DevSecOps pipelines.
Additional Skills & Qualifications
Ideally would like someone in South FL that could come to office 1-2x per month
Strong communication and proactive attitude
NEE is stable, long term projects with lots of project work
Business Drivers/Customer Impact
deploying AI & ML models to AWS Cloud
Why is the position open?
Currently data scientists do not have someone to own the deployment and data pipelines but need this feature available
External Communities Job Description
90% remote MLOps Data Engineer who will focus on building data pipelines and deploying AI/ML models to AWS
Job Title: MLOps Engineer
Location: Remote (Juno Beach, FL)- 90%
Top Skills - Must Haves
Python
Aws
data engineering
machine learning
Top Skills
3+ years of experience working on building and deploying data pipelines in AWS- lambda, S3, ECS
1+ year of experience deploying ML and AI models to AWS - creating the DevOps pipelines to deploy
Experience developing in Python
Summary
We are seeking a highly skilled MLOps Engineer with deep expertise in Amazon Web Services (AWS) to design, build, automate, and maintain machine learning infrastructure and deployment pipelines. This role bridges the gap between Data Science, Software Engineering, and Cloud Operations, ensuring that machine learning models are deployed, monitored, and scaled efficiently in production environments.
The ideal candidate will have experience implementing CI/CD for machine learning, managing cloud-native architectures, containerization, infrastructure as code, and supporting the full machine learning lifecycle using AWS services.
Key Responsibilities
Machine Learning Operations
Design, implement, and maintain scalable MLOps pipelines for model training, testing, deployment, and monitoring.
Automate ML workflows from data ingestion through model deployment and retraining.
Establish model versioning, experiment tracking, and governance processes.
Build and maintain feature stores and model registries.
AWS Cloud Engineering
Develop and manage machine learning infrastructure using AWS services such as:
Amazon SageMaker
AWS Lambda
Amazon ECS/EKS
EC2
S3
Glue
Redshift
CloudWatch
Step Functions
AWS CodePipeline
AWS CodeBuild
CI/CD & Automation
Build CI/CD pipelines for ML applications and data science workflows.
Implement Infrastructure as Code (IaC) using Terraform or AWS CloudFormation.
Automate deployment, testing, rollback, and monitoring processes.
Integrate security and compliance controls into DevSecOps pipelines.
Additional Skills & Qualifications
Ideally would like someone in South FL that could come to office 1-2x per month
Strong communication and proactive attitude
NEE is stable, long term projects with lots of project work
Business Drivers/Customer Impact
deploying AI & ML models to AWS Cloud
Why is the position open?
Currently data scientists do not have someone to own the deployment and data pipelines but need this feature available
External Communities Job Description
90% remote MLOps Data Engineer who will focus on building data pipelines and deploying AI/ML models to AWS




