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Senior Machine Learning Ops Engineer Jobs in Missouri

As a Machine Learning Engineer, you will work with complex datasets, design and optimize models, and help bring intelligent solutions into production. You will collaborate with software engineers and ...

Our partner is looking for a Machine Learning Engineer - Distillation based in Netherlands. This ... Collaboration with a small, senior team combining research expertise and engineering excellence.

So what's the job As a Senior Machine Learning Data Scientist in the Data Team at Catawiki you will ... Working with cross-disciplinary teams involving product owners, developers, UX designers, and ...

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...

As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart ...

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Senior Machine Learning Ops Engineer information

What are the key skills and qualifications needed to thrive as a senior machine learning ops engineer?

To thrive as a Senior Machine Learning Ops Engineer, you need expertise in machine learning, software engineering, cloud platforms, and experience with CI/CD pipelines, often supported by a computer science degree or equivalent experience. Proficiency with tools like Docker, Kubernetes, TensorFlow, PyTorch, and cloud services such as AWS, GCP, or Azure is typically required, along with familiarity with MLOps frameworks. Strong problem-solving, collaboration, and communication skills help you work effectively with cross-functional teams and manage complex ML model deployments. These skills are essential to ensure reliable, scalable, and efficient deployment of machine learning models in production environments.

What are some common challenges faced by senior machine learning ops engineers when deploying models to production?

Senior Machine Learning Ops Engineers often encounter challenges such as ensuring model reproducibility, managing model versioning, and automating deployment pipelines for scalability. Another key challenge is monitoring model performance and data drift in production, which requires robust logging and alerting systems. Collaborating closely with data scientists, software engineers, and IT teams is essential to address these challenges and maintain a stable, efficient ML infrastructure.

What is the difference between Senior Machine Learning Ops Engineer vs Data Engineer?

AspectSenior Machine Learning Ops EngineerData Engineer
CredentialsExperience with ML frameworks, cloud platforms, scripting, and DevOps toolsStrong SQL, ETL, database, and programming skills, often with cloud experience
Work EnvironmentFocus on deploying, monitoring, and maintaining ML models in productionDesigning and building data pipelines and infrastructure for data processing
Industry UsageCommon in AI/ML-focused companies, tech firms, and data-driven organizationsWidespread across industries for data management and analytics

While both roles involve working with data and cloud platforms, the Senior Machine Learning Ops Engineer specializes in deploying and maintaining machine learning models, whereas the Data Engineer focuses on building data pipelines and infrastructure. Understanding these distinctions helps in choosing the right career path or job search focus.

What is a senior machine learning ops engineer?

Senior Machine Learning Ops (MLOps) Engineers are experienced professionals who design, build, and maintain the infrastructure and tools needed to deploy, monitor, and scale machine learning models in production environments. They work at the intersection of data science, software engineering, and DevOps to ensure ML models are robust, reliable, and secure. Their responsibilities often include automating model training pipelines, managing cloud resources, implementing CI/CD for ML, and ensuring model reproducibility. Senior MLOps Engineers also mentor junior staff and help define best practices for the organization’s ML workflow.
What are the most commonly searched types of Machine Learning Ops Engineer jobs in Missouri? The most popular types of Machine Learning Ops Engineer jobs in Missouri are:
What are popular job titles related to Senior Machine Learning Ops Engineer jobs in Missouri? For Senior Machine Learning Ops Engineer jobs in Missouri, the most frequently searched job titles are:
What cities in Missouri are hiring for Senior Machine Learning Ops Engineer jobs? Cities in Missouri with the most Senior Machine Learning Ops Engineer job openings:
Infographic showing various Senior Machine Learning Ops Engineer job openings in Missouri as of August 2026, with employment types broken down into 1% As Needed, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution.

Machine Learning Engineer

Jobgether

On-site, Remote

Full-time

Posted 9 days ago


Job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Machine Learning Engineer based in Netherlands.

This role offers the opportunity to build and improve AI-powered products by developing machine learning solutions that address real-world challenges.
As a Machine Learning Engineer, you will work with complex datasets, design and optimize models, and help bring intelligent solutions into production.
You will collaborate with software engineers and technical teams in a fast-moving, remote environment focused on innovation.
The position combines data analysis, experimentation, software development, and machine learning engineering to create impactful products.
You will have the opportunity to work on challenging technical problems while continuously expanding your expertise in emerging AI technologies.
This role is ideal for a curious and motivated engineer who enjoys building practical AI applications and solving meaningful problems.

Accountabilities:

The Machine Learning Engineer will contribute to the development, deployment, and improvement of AI-driven solutions. The role requires strong technical skills, analytical thinking, and a passion for building scalable machine learning systems. Key responsibilities include:

  • Develop, train, test, and evaluate machine learning models to support product objectives.
  • Prepare, clean, analyze, and manage datasets used for model training and validation.
  • Improve model performance through experimentation, optimization, and continuous testing.
  • Deploy, maintain, and monitor machine learning models in production environments.
  • Collaborate with software engineers to integrate machine learning capabilities into products and applications.
  • Track model performance and identify opportunities to improve accuracy, reliability, and efficiency.
  • Apply modern machine learning techniques and stay informed about emerging technologies and best practices.
  • Write clean, maintainable, and efficient code to support scalable AI solutions.
  • Contribute to technical discussions and help solve complex engineering challenges.
  • Participate in the continuous improvement of machine learning workflows and development processes.
Requirements:

The ideal candidate is a technically curious and motivated professional with a foundation in machine learning, software engineering, or data science. Required qualifications and skills include:

  • 1+ year of experience in machine learning, software engineering, data science, or a related technical field, or strong personal projects demonstrating machine learning capabilities.
  • Basic understanding of machine learning concepts, algorithms, and model development processes.
  • Strong programming skills in Python.
  • Familiarity with machine learning frameworks and libraries such as PyTorch, TensorFlow, or scikit-learn.
  • Comfortable working with data and writing clean, maintainable code.
  • Experience using Git and version control practices.
  • Strong problem-solving skills with the ability to analyze technical challenges.
  • Good written and verbal English communication skills.
  • Passion for learning, experimenting, and building AI-powered solutions.
  • Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field is a plus but not mandatory.
  • Experience with large language models (LLMs) is an advantage.
  • Familiarity with cloud platforms such as AWS, GCP, or Azure is beneficial.
  • Knowledge of SQL and experience deploying machine learning models are considered advantages.
  • Personal AI, machine learning projects, or open-source contributions are highly valued.
Benefits:

The role offers a flexible remote environment and the opportunity to contribute to innovative AI solutions while growing professionally. Benefits include:

  • Fully remote work opportunity.
  • Flexible working environment with autonomy and work-life balance.
  • Opportunity to build and improve real-world AI products.
  • Exposure to challenging technical problems and modern machine learning technologies.
  • Collaborative and fast-moving team environment.
  • Opportunities for professional growth and continuous learning.
  • Competitive compensation based on experience.
  • Opportunity to contribute to impactful machine learning projects from anywhere.
How Jobgether works:
We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
We appreciate your interest and wish you the best!
 Why Apply Through Jobgether? 
 
Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.
 
 
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We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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