

FundBnk
AI Engineer / Machine Learning Engineer – MLOps
⭐ - Featured Role | Apply direct with Data Freelance Hub
This role is for an AI Engineer / Machine Learning Engineer – MLOps, offering a remote contract with a competitive pay rate. Requires 3–7 years in Machine Learning, 2+ years in MLOps, proficiency in Python, Docker, Kubernetes, and cloud platforms.
🌎 - Country
United States
💱 - Currency
$ USD
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💰 - Day rate
Unknown
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🗓️ - Date
April 7, 2026
🕒 - Duration
Unknown
-
🏝️ - Location
Remote
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📄 - Contract
Unknown
-
🔒 - Security
Unknown
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📍 - Location detailed
United States
-
🧠 - Skills detailed
#Model Deployment #Data Quality #Data Science #Data Engineering #Databases #Docker #ML Ops (Machine Learning Operations) #Flask #Kubernetes #Security #ML (Machine Learning) #Jenkins #GitHub #Logging #Monitoring #Scala #Azure #AI (Artificial Intelligence) #Data Pipeline #Airflow #GitLab #Deployment #AWS (Amazon Web Services) #Terraform #NoSQL #MLflow #Cloud #Python #SageMaker #GCP (Google Cloud Platform) #FastAPI #SQL (Structured Query Language)
Role description
This is a remote position.
AI Engineer / Machine Learning Engineer – MLOps
We are looking for an AI Engineer with strong experience in Machine Learning Operations (MLOps) to design, deploy, monitor, and maintain machine learning and AI models in production environments. The candidate will be responsible for building scalable ML pipelines, automating model deployment, managing model lifecycle, and ensuring reliability, performance, and governance of AI systems.
Key Responsibilities
• Build and maintain ML pipelines for training, testing, and deployment
• Deploy machine learning and AI models into production environments
• Manage model lifecycle (training, deployment, monitoring, retraining)
• Automate workflows using CI/CD for ML models
• Monitor model performance, drift, and data quality
• Work with data scientists and AI developers to productionize models
• Manage model versioning, data versioning, and experiment tracking
• Deploy models on cloud platforms (AWS, Azure, GCP)
• Containerize applications using Docker and Kubernetes
• Implement monitoring and logging for ML systems
• Ensure scalability, security, and reliability of AI systems
Requirements
Required Skills
• Python
• Machine Learning
• MLOps tools and frameworks
• Docker
• Kubernetes
• CI/CD (GitHub Actions, Jenkins, GitLab CI)
• MLflow / Kubeflow / Airflow
• Data pipelines
• APIs (FastAPI / Flask)
• Cloud platforms (AWS / Azure / GCP)
• SQL / NoSQL databases
• Model monitoring and logging
MLOps Tools (Important)
Candidate should have experience in some of these:
• MLflow
• Kubeflow
• Airflow
• DVC
• Weights & Biases
• SageMaker
• Azure ML
• Vertex AI
• Docker
• Kubernetes
• Terraform
Experience Required
• 3–7 years in Machine Learning / AI / Data Engineering
• 2+ years in MLOps / Model Deployment / ML Pipelines
• Experience deploying models to production is mandatory
Education
Benefits
• Competitive compensation package
• Opportunities for professional development and career advancement.
• Flexible working conditions, with remote options available.
• Dynamic and supportive work environment.
Equal Employment Opportunity
KATBOTZ LLC is an Equal Opportunity Employer. We provide equal employment opportunities to all qualified individuals, regardless of race, religion, gender, gender identity, age, marital status, national origin, sexual orientation, citizenship status, veteran status, disability, or any other legally protected status. As an organization, we are unwavering in our commitment to maintaining a discrimination-free work environment, and fostering a culture of inclusivity, belonging and equal opportunity for all employees and applicants.
This is a remote position.
AI Engineer / Machine Learning Engineer – MLOps
We are looking for an AI Engineer with strong experience in Machine Learning Operations (MLOps) to design, deploy, monitor, and maintain machine learning and AI models in production environments. The candidate will be responsible for building scalable ML pipelines, automating model deployment, managing model lifecycle, and ensuring reliability, performance, and governance of AI systems.
Key Responsibilities
• Build and maintain ML pipelines for training, testing, and deployment
• Deploy machine learning and AI models into production environments
• Manage model lifecycle (training, deployment, monitoring, retraining)
• Automate workflows using CI/CD for ML models
• Monitor model performance, drift, and data quality
• Work with data scientists and AI developers to productionize models
• Manage model versioning, data versioning, and experiment tracking
• Deploy models on cloud platforms (AWS, Azure, GCP)
• Containerize applications using Docker and Kubernetes
• Implement monitoring and logging for ML systems
• Ensure scalability, security, and reliability of AI systems
Requirements
Required Skills
• Python
• Machine Learning
• MLOps tools and frameworks
• Docker
• Kubernetes
• CI/CD (GitHub Actions, Jenkins, GitLab CI)
• MLflow / Kubeflow / Airflow
• Data pipelines
• APIs (FastAPI / Flask)
• Cloud platforms (AWS / Azure / GCP)
• SQL / NoSQL databases
• Model monitoring and logging
MLOps Tools (Important)
Candidate should have experience in some of these:
• MLflow
• Kubeflow
• Airflow
• DVC
• Weights & Biases
• SageMaker
• Azure ML
• Vertex AI
• Docker
• Kubernetes
• Terraform
Experience Required
• 3–7 years in Machine Learning / AI / Data Engineering
• 2+ years in MLOps / Model Deployment / ML Pipelines
• Experience deploying models to production is mandatory
Education
Benefits
• Competitive compensation package
• Opportunities for professional development and career advancement.
• Flexible working conditions, with remote options available.
• Dynamic and supportive work environment.
Equal Employment Opportunity
KATBOTZ LLC is an Equal Opportunity Employer. We provide equal employment opportunities to all qualified individuals, regardless of race, religion, gender, gender identity, age, marital status, national origin, sexual orientation, citizenship status, veteran status, disability, or any other legally protected status. As an organization, we are unwavering in our commitment to maintaining a discrimination-free work environment, and fostering a culture of inclusivity, belonging and equal opportunity for all employees and applicants.






