... of MLOps and tools like Docker and Kubernetes for productionizing models • Familiar with AWS, Google Cloud, or Azure for deploying, scaling and managing AI models Company : Mercer Engineering ...
... of MLOps and tools like Docker and Kubernetes for productionizing models • Familiar with AWS, Google Cloud, or Azure for deploying, scaling and managing AI models Company : Mercer Engineering ...
Define and govern enterprise AI architecture standards , including model lifecycle management, MLOps, and AI platform integration. * Ensure responsible and compliant AI adoption, aligned with AI ...
Define and govern enterprise AI architecture standards , including model lifecycle management, MLOps, and AI platform integration. * Ensure responsible and compliant AI adoption, aligned with AI ...
Mlops information
What is 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 are the key skills and qualifications needed to thrive as an MLOps engineer?
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 is the difference between Mlops vs Data Engineer?
| Aspect | Mlops | Data Engineer |
|---|---|---|
| Primary Focus | Deploying, managing, and monitoring machine learning models in production | Building and maintaining data pipelines and infrastructure for data processing |
| Skills & Certifications | Machine learning, DevOps, cloud platforms, scripting | SQL, ETL, data warehousing, programming |
| Work Environment | Collaborates with data scientists, software engineers, and DevOps teams | Works with data analysts, data scientists, and software developers |
| Industry Usage | AI/ML projects, production environments, cloud services | Data 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.
Is MLOps in demand?
MLOps is a rapidly growing field as organizations increasingly adopt machine learning models in production. Professionals with skills in cloud platforms, automation, and tools like Kubernetes and Docker are highly sought after, reflecting strong industry demand for MLOps expertise.
Is MLOps outdated?
MLOps is an evolving field focused on deploying and managing machine learning models efficiently. It remains highly relevant as organizations increasingly adopt AI solutions, with skills in automation, cloud platforms, and monitoring tools in demand. Staying current with new tools and best practices is essential for MLOps professionals.
What is the average salary in MLOps?
The average salary for MLOps engineers typically ranges from $100,000 to $150,000 annually, depending on experience, location, and company size. Professionals with skills in cloud platforms, automation, and machine learning deployment tend to earn higher salaries.
What cities near Perry, GA are hiring for Mlops jobs?
Cities near Perry, GA with the most Mlops job openings:
Full-time
Re-posted 24 days ago
Job description
Job Summary:
Mercer Engineering Research Center is seeking a Senior Computer Scientist to lead, design, and perform detailed software development activities. The role involves overseeing the entire software development process, from concept to deployment, while mentoring less experienced team members and ensuring adherence to coding standards.
Responsibilities:
• Serve as internal resource on structured programming techniques for software systems.
• Lead program design reviews with customer.
• Responsible from the initial concept to design, coding, and testing to meet specifications.
• Perform analysis of algorithms and requirements for computational resources and architecture required.
• Lead a team to develop software systems, including the design, implementation and deployment of device drivers, kernel enhancements, and other essential system-level software.
• Lead a team to perform analysis, design, software development, and maintenance tasks for highly complex systems.
• Coach less experienced computer scientists and ensure adherence to design and coding standards and practices.
• Mentor and lead less experienced team members.
• Lead the development, test, and integration of code for new or existing software of significant complexity involving multiple teams.
• Remain abreast of all applicable technical advancements.
• Apply and adapt theoretical principles to develop new computer software and/or hardware solutions.
• Develop approaches to solve analytical problems and document methodologies
Qualifications:
Required:
• US Citizenship.
• Ability to obtain and maintain a DoD Security Clearance.
• An undergraduate degree in computer science and 9 years of directly related experience; a Master’s degree in same and 8 years of directly related experience; or a Ph.D. degree in same and 3 years of directly related experience.
• 3 years of technical lead responsibility.
• Ability to obtain certification in accordance with DoD 8140 series requirements.
• Proficient using Python, Java, JavaScript, J-Query, C#, and Dot.NET frameworks including MVC, Web Forms, and/or Dot.NET core.
• Proficient in secure coding practices.
• Proficient in database design principles and Structured Query Language (SQL).
• Proficient in agile framework and software development life cycle.
• Proficient in the developing testing strategies for components and/or applications.
Preferred:
• Proficient in TensorFlow, PyTorch, or similar libraries
• Experience building, fine tuning, and deploying advanced models (e.g., LLMs, computer vision, recommendation systems)
• Designing scalable AI pipelines, integrating data ingestion, model training, and production deployment
• Familiarity with CI/CD for models and model versioning
• Skilled in feature engineering, handling large datasets, and applying vector databases for semantic search
• Ability to interpret state of the art AI research and implement findings in production systems
• Understanding of model explainability, bias mitigation, and ethical AI practices
• Strong communication skills to work with cross functional teams and mentor junior engineers in AI practices
• Familiar with data manipulation, cleaning and analysis.
• Familiar with Oracle/SQL Server
• Knowledge of MLOps and tools like Docker and Kubernetes for productionizing models
• Familiar with AWS, Google Cloud, or Azure for deploying, scaling and managing AI models
Company:
Mercer Engineering Research Center is a non-profit engineering and technology services provider company. Founded in 1987, the company is headquartered in Warner Robins, USA, with a team of 201-500 employees. The company is currently Growth Stage.
Mercer Engineering Research Center is seeking a Senior Computer Scientist to lead, design, and perform detailed software development activities. The role involves overseeing the entire software development process, from concept to deployment, while mentoring less experienced team members and ensuring adherence to coding standards.
Responsibilities:
• Serve as internal resource on structured programming techniques for software systems.
• Lead program design reviews with customer.
• Responsible from the initial concept to design, coding, and testing to meet specifications.
• Perform analysis of algorithms and requirements for computational resources and architecture required.
• Lead a team to develop software systems, including the design, implementation and deployment of device drivers, kernel enhancements, and other essential system-level software.
• Lead a team to perform analysis, design, software development, and maintenance tasks for highly complex systems.
• Coach less experienced computer scientists and ensure adherence to design and coding standards and practices.
• Mentor and lead less experienced team members.
• Lead the development, test, and integration of code for new or existing software of significant complexity involving multiple teams.
• Remain abreast of all applicable technical advancements.
• Apply and adapt theoretical principles to develop new computer software and/or hardware solutions.
• Develop approaches to solve analytical problems and document methodologies
Qualifications:
Required:
• US Citizenship.
• Ability to obtain and maintain a DoD Security Clearance.
• An undergraduate degree in computer science and 9 years of directly related experience; a Master’s degree in same and 8 years of directly related experience; or a Ph.D. degree in same and 3 years of directly related experience.
• 3 years of technical lead responsibility.
• Ability to obtain certification in accordance with DoD 8140 series requirements.
• Proficient using Python, Java, JavaScript, J-Query, C#, and Dot.NET frameworks including MVC, Web Forms, and/or Dot.NET core.
• Proficient in secure coding practices.
• Proficient in database design principles and Structured Query Language (SQL).
• Proficient in agile framework and software development life cycle.
• Proficient in the developing testing strategies for components and/or applications.
Preferred:
• Proficient in TensorFlow, PyTorch, or similar libraries
• Experience building, fine tuning, and deploying advanced models (e.g., LLMs, computer vision, recommendation systems)
• Designing scalable AI pipelines, integrating data ingestion, model training, and production deployment
• Familiarity with CI/CD for models and model versioning
• Skilled in feature engineering, handling large datasets, and applying vector databases for semantic search
• Ability to interpret state of the art AI research and implement findings in production systems
• Understanding of model explainability, bias mitigation, and ethical AI practices
• Strong communication skills to work with cross functional teams and mentor junior engineers in AI practices
• Familiar with data manipulation, cleaning and analysis.
• Familiar with Oracle/SQL Server
• Knowledge of MLOps and tools like Docker and Kubernetes for productionizing models
• Familiar with AWS, Google Cloud, or Azure for deploying, scaling and managing AI models
Company:
Mercer Engineering Research Center is a non-profit engineering and technology services provider company. Founded in 1987, the company is headquartered in Warner Robins, USA, with a team of 201-500 employees. The company is currently Growth Stage.
About Mercer Engineering Research Center
Sourced by ZipRecruiter
Industry
Scientific research and development services
Company size
51 - 200 Employees
Headquarters location
Warner Robins, GA, US
Year founded
1987