ZipRecruiter

Log In

1

Mlops Engineer Jobs in Missouri (NOW HIRING)

Codinix Consulting Services

MLOps Engineer

California, MO · On-site

$120 - $150/hr

Local candidates only Job Overview We are seeking an experienced MLOps Engineer to design, build, and maintain scalable machine learning operations pipelines that support the full model lifecycle ...

New

Mylitm

MLOps Engineer

California, MO · On-site

$140 - $210/hr

Autonomous Decisioning Research & Engineering About this role You will build and operate the platform that takes our models from research notebooks to reliable, observable production services. You ...

New

Kohl's

$95K - $131K/yr

Role Specific Information About the Role As Senior MLOps Engineer, you will focus on supporting cross-functional teams in designing, deploying, and operating machine learning solutions while building ...

Jobtailor

Software Engineer, ML Platform

California, MO · On-site

$140 - $200/hr

Build core components of the ML and AI Platform technical roadmap, including MLOps solutions with ... Apply AI tools in the engineering workflow and bring an AI-native lens to engineering and product ...

New

Koantek

Senior AI Engineer

Chesterfield, MO · Remote

$54.75 - $70.50/hr

Sr AI Engineer / Data Scientist / MLOps Consultant Location: United States - Remote Employment Type: Full-Time and Contract We are seeking an experienced and highly technical Data Scientist to join ...

Koantek

Senior AI Engineer

Chesterfield, MO · On-site

$54.75 - $70.50/hr

Sr AI Engineer / Data Scientist / MLOps Consultant Location: United States - Remote Employment Type: Full-Time and Contract We are seeking an experienced and highly technical Data Scientist to join ...

KlearNow.ai

Senior AI Engineer

California, MO · On-site

$187 - $215/hr

MLOps / DevOps experience; CI/CD pipelines, Docker, Kubernetes * Former founder or early employee at a startup Why This Role * Define the core AI systems of the company from the ground up * Work on ...

Equifax

AI Engineer

Saint Louis, MO · On-site

Champion MLOps for Agentic Systems: Establish and lead best practices for the reliability ... Define and report on key engineering metrics (SLA, SLO, SLI) and ensure compliance with security ...

Equifax, Inc.

AI Engineer

Saint Louis, MO · On-site

$99K - $131K/yr

Champion MLOps for Agentic Systems: Establish and lead best practices for the reliability ... Define and report on key engineering metrics (SLA, SLO, SLI) and ensure compliance with security ...

MasterCard

Senior AI Engineer

O Fallon, MO · On-site

$97K - $134K/yr

... MLOps practices to improve reliability, efficiency, and scalability. -Develop AI-powered solutions that address engineering challenges and streamline operational processes. -Partner with Data ...

next page

Showing results 1-20

All JobsMlops Engineer JobsMlops Engineer Jobs in Missouri

Mlops Engineer information

What is an MLOps engineer?

An MLOps Engineer is responsible for deploying, monitoring, and maintaining machine learning models in production. They bridge the gap between data science and operations by automating workflows, optimizing infrastructure, and ensuring model reliability. Their role includes CI/CD for ML models, data pipeline management, and performance monitoring. They also work with cloud platforms, containerization, and orchestration tools to scale ML systems efficiently.

What are some common challenges MLOps engineers face in their daily work?

Mlops Engineers often encounter challenges in integrating new machine learning models into existing production systems while ensuring minimal downtime and maintaining data integrity. Managing the scaling and orchestration of models across various cloud or on-prem environments can be complex, requiring close coordination with data scientists and DevOps teams. Staying up to date with rapidly evolving tools and best practices is also essential in this field. Addressing these challenges provides valuable opportunities to innovate and improve both technical processes and team collaboration.

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 strong skills in software engineering, machine learning pipelines, and cloud infrastructure, often backed by a degree in computer science, engineering, or a related field. Familiarity with tools such as Docker, Kubernetes, TensorFlow, AWS/GCP/Azure, and CI/CD systems is essential, and certifications like AWS Certified Machine Learning or Kubernetes Administrator are often valued. Effective communication, problem-solving, and teamwork are crucial soft skills for collaborating across data science and IT teams. These abilities enable Mlops Engineers to efficiently deploy, manage, and scale machine learning models in dynamic production environments.

What do you need to be a MLOps engineer?

To become a MLOps engineer, you typically need a strong background in software engineering, machine learning, and cloud platforms. Proficiency in programming languages like Python, experience with containerization tools such as Docker, and knowledge of CI/CD pipelines are essential. Certifications in cloud services and familiarity with tools like Kubernetes and ML frameworks also enhance qualifications.

Who earns more, ML engineer or MLOps engineer?

MLOps engineers typically earn slightly more than ML engineers due to their focus on deploying, maintaining, and scaling machine learning systems, which requires expertise in cloud platforms, automation, and infrastructure. Salary differences can vary based on experience, location, and company size, but MLOps roles often command higher compensation because of their specialized skill set.

What are the most commonly searched types of Mlops Engineer jobs in Missouri?

The most popular types of Mlops Engineer jobs in Missouri are:

What are popular job titles related to Mlops Engineer jobs in Missouri?

For Mlops Engineer jobs in Missouri, the most frequently searched job titles are:

What job categories do people searching Mlops Engineer jobs in Missouri look for?

The top searched job categories for Mlops Engineer jobs in Missouri are:

Mlops Engineer jobs near you
Infographic showing various Mlops Engineer job openings in Missouri as of August 2026, with employment types broken down into 90% Full Time, 5% Part Time, and 5% Contract. Highlights an 84% Physical, 6% Hybrid, and 10% Remote job distribution.

MLOps Engineer

Codinix Consulting Services

California, MO • On-site

$120 - $150/hr

Other

Posted 2 days ago

New

Job description

Location: SFO, California, Duration: Long-Term Contract, Note: Local candidates only

Job Overview

We are seeking an experienced MLOps Engineer to design, build, and maintain scalable machine learning operations pipelines that support the full model lifecycle—from development and training to deployment, monitoring, and retraining. This role focuses on enabling production-grade ML systems using modern cloud platforms, CI/CD practices, and MLOps frameworks, ensuring reliability, scalability, governance, and performance of machine learning models in enterprise environments.

Key Responsibilities

ML Pipeline Development & Operations

  • Develop and maintain robust machine learning pipelines using frameworks such as MLflow, Kubeflow, or Vertex AI.
  • Automate the end-to-end ML lifecycle, including model training, validation, testing, deployment, and monitoring in cloud environments.
  • Implement reusable and scalable workflows for model versioning, tracking, and retraining.

CI/CD & Model Lifecycle Management

  • Design and implement CI/CD pipelines for machine learning models, ensuring seamless integration from development to production.
  • Manage model versioning, model registry, and deployment pipelines with strong governance practices.
  • Ensure reproducibility and traceability across ML lifecycle stages.

Cloud, Containers & Deployment

  • Deploy and manage ML workloads on cloud platforms such as GCP, AWS, or Azure.
  • Work with containerization technologies like Docker and Kubernetes to provision scalable model serving environments.
  • Enable low-latency model scoring APIs for real-time inference use cases.

Monitoring, Governance & Compliance

  • Implement model monitoring and observability frameworks to track performance, drift, and anomalies in production.
  • Ensure compliance with model risk management (MRM) standards, including documentation, explainability, and audit readiness.
  • Establish alerts and feedback loops for continuous model improvement and retraining.

Collaboration & Engineering Enablement

  • Collaborate with data engineering and platform teams to build and optimize data pipelines and ML infrastructure.
  • Support engineering teams in provisioning scalable environments for ML model development and deployment.
  • Partner with stakeholders to translate business requirements into ML-driven solutions.

AutoML & Accelerated ML Development

  • Leverage AutoML tools such as Vertex AI AutoML and H2O Driverless AI to accelerate model development and deployment.
  • Enable low-code/no-code ML workflows where appropriate, while ensuring production-grade quality and governance.
Required Qualifications
  • 10+ years of experience in Software Engineering, with at least 3+ years focused on AI/ML and MLOps.
  • Strong programming experience in Python and Java, along with SQL and ML libraries such as scikit-learn, XGBoost, TensorFlow, or PyTorch.
  • Hands-on experience with cloud platforms (GCP, AWS, or Azure).
  • Strong knowledge of containerization technologies (Docker, Kubernetes).
  • Experience with data engineering and workflow orchestration tools such as Airflow and Spark.
  • Solid understanding of DevOps principles, CI/CD practices, and software engineering best practices.
  • Strong communication skills with the ability to explain complex ML concepts to both technical and non-technical stakeholders.
Preferred Qualifications
  • Experience with Vertex AI, MLflow, Kubeflow, or similar MLOps platforms.
  • Familiarity with model governance frameworks (MRM, model documentation, explainability tools).
  • Experience building real-time inference systems and scalable ML APIs.
  • Exposure to enterprise-scale ML systems in regulated industries.
#J-18808-Ljbffr

Apply