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Mlops Jobs in Decatur, GA (NOW HIRING)

Juvare

Senior DevOps Engineer

Atlanta, GA · On-site +1

$125K - $160.60K/yr

Experience supporting AI/MLOps workflows is a plus. Location * Atlanta / Remote Must Have * Cloud Platforms: Strong hands-on experience with AWS and Azure * Regulated Environments: Experience ...

Purple Drive Technologies

PYTHON DEVELOPER

Alpharetta, GA · On-site

$49 - $67.50/hr

... MLOps teams to translate requirements into SDK features • Write comprehensive unit, integration, and contract tests • Create and maintain developer documentation, examples, and notebooks • ...

OptiTrack

Machine Learning & Operations Engineer

Atlanta, GA · On-site +1

$66.90K - $90.50K/yr

Establish and enforce MLOps best practices including model registry, artifact management, and observability. * Improve system reliability, performance, and security. * Collaborate closely with ML ...

OptiTrack

Machine Learning & Operations Engineer

Atlanta, GA · Remote

$66.80K - $90.40K/yr

Establish and enforce MLOps best practices including model registry, artifact management, and observability. * Improve system reliability, performance, and security. * Collaborate closely with ML ...

OptiTrack

Machine Learning & Operations Engineer

Atlanta, GA · Remote

$71.10K - $96.20K/yr

Establish and enforce MLOps best practices including model registry, artifact management, and observability. * Improve system reliability, performance, and security. * Collaborate closely with ML ...

Patientco

Senior ML Engineer II

Atlanta, GA

$100.50K - $138K/yr

MLOps & Deployment: * Deploy, manage, and monitor LMs and agentic components on Google Cloud Platform (GCP) using services like Vertex AI, GKE, Cloud Functions, and Cloud Run. * Implement robust ...

ConstructConnect

Machine Learning Engineer II

Atlanta, GA

$93.80K - $128.40K/yr

You bring a strong engineering background (cloud, infrastructure, CI/CD, MLOps) and are excited to create "paved paths" for software engineers to use AI tools, models, and patterns safely and ...

Crew Career Center

Senior ML Engineer II

Atlanta, GA

$100.50K - $138K/yr

MLOps & Deployment: * Deploy, manage, and monitor LMs and agentic components on Google Cloud Platform (GCP) using services like Vertex AI, GKE, Cloud Functions, and Cloud Run. * Implement robust ...

Waystar

Senior ML Engineer II

Atlanta, GA · On-site

$100.50K - $138K/yr

MLOps & Deployment: * Deploy, manage, and monitor LMs and agentic components on Google Cloud Platform (GCP) using services like Vertex AI, GKE, Cloud Functions, and Cloud Run. * Implement robust ...

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Mlops information

What are the key skills and qualifications needed to thrive as an MLOps Engineer, and why are they important?

To thrive as an MLOps Engineer, you need a strong background in machine learning, software engineering, and DevOps principles, often supported by a degree in computer science or a related field. Proficiency with tools like Docker, Kubernetes, CI/CD pipelines, cloud platforms (e.g., AWS, Azure, GCP), and ML frameworks is typically required, along with certifications in cloud or DevOps technologies. Strong problem-solving skills, collaboration, and communication abilities help MLOps professionals excel in cross-functional teams and manage complex workflows. These skills are vital for reliably deploying, monitoring, and scaling machine learning models in production environments, ensuring efficiency and robustness.

What are some common challenges faced by MLOps professionals when deploying machine learning models to production?

MLOps professionals often encounter challenges such as ensuring reproducibility of models, managing version control for both code and data, and maintaining model performance over time. Handling continuous integration and deployment (CI/CD) pipelines for ML models can be complex, especially when dealing with large datasets and evolving algorithms. Additionally, coordinating with data scientists, software engineers, and DevOps teams to streamline workflows and monitor models post-deployment are key responsibilities that require both technical expertise and strong collaboration skills.

What are MLOps?

MLOps, short for Machine Learning Operations, is a set of practices that combines machine learning, DevOps, and data engineering to automate and streamline the deployment, monitoring, and maintenance of machine learning models in production. MLOps aims to improve collaboration between data scientists and operations teams, ensuring that models are robust, scalable, and easily updated. It covers the entire machine learning lifecycle, from data preparation to model training, deployment, and ongoing monitoring. By implementing MLOps, organizations can accelerate the development and deployment of reliable machine learning solutions.

What is the difference between Mlops vs Data Engineer?

AspectMlopsData Engineer
Primary FocusDeploying, managing, and monitoring machine learning models in productionBuilding and maintaining data pipelines and infrastructure for data processing
Skills & CertificationsMachine learning, DevOps, cloud platforms, scriptingSQL, ETL, data warehousing, programming
Work EnvironmentCollaborates with data scientists, software engineers, and DevOps teamsWorks with data analysts, data scientists, and software developers
Industry UsageAI/ML projects, production environments, cloud servicesData infrastructure, analytics, big data processing

While both Mlops and Data Engineers work closely with data and cloud technologies, Mlops specialists focus on deploying and maintaining machine learning models in production, ensuring their scalability and reliability. Data Engineers primarily build data pipelines and infrastructure to support data analysis and ML workflows. Understanding these distinctions helps organizations assign the right roles for their AI and data projects.

What are the most commonly searched types of Mlops jobs in Decatur, GA? The most popular types of Mlops jobs in Decatur, GA are:
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What cities near Decatur, GA are hiring for Mlops jobs? Cities near Decatur, GA with the most Mlops job openings:
Mlops jobs near you

Data Engineer: Snowflake

Futran Tech Solutions Pvt. Ltd.

Atlanta, GA • On-site

$110.10K - $132.20K/yr

Full-time

Posted 26 days ago

Job description

Job ID : 653779
Atlanta - Georgia - USA
Data Engineer: Snowflake
Responsibilities:
• Data Pipeline Development: Design, build, and maintain scalable ETL pipelines to ensure efficient data flow from diverse sources into data warehouses.
• Workflow Automation: Utilize Apache Airflow for scheduling and managing complex data workflows.
• Collaboration: Partner with data scientists to implement machine learning models (MLOps) in production.
• Coding and Scripting: Write high-quality Python code for data extraction, transformation, and loading tasks.
• Big Data Processing: Use Databricks for processing large datasets and optimizing data workflows.
• Data Quality Management: Ensure data accuracy and integrity by implementing monitoring, logging, and ing mechanisms.
• Version Control: Use GitHub for code management and collaboration, maintaining clear documentation of data processes.
• Database Management: Work with Snowflake and BigQuery for data storage and analytics, ensuring optimal performance and scalability.
• Performance Optimization: Analyze and tune data storage solutions and SQL queries for efficiency.
• Cross-Functional Support: Collaborate with business units to identify and fulfill data needs effectively.
Basic Qualifications:
• 3+ years of experience in data engineering or a related field.
• Proficient in Python for data processing and scripting.
• Experience with Apache Airflow or similar workflow management tools.
• Familiarity with machine learning operations (MLOps) and model deployment.
• Solid understanding of cloud platforms (AWS, Azure, GCP).
• Strong SQL skills and experience with data modeling.
• Practical experience with Snowflake and BigQuery.
Preferred Skills:
• Knowledge of Databricks for data processing and analytics.
• Experience with data visualization tools like Tableau or Power BI.
• Familiarity with containerization technologies such as Docker.
• Understanding of DataOps principles and practices.

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