ZipRecruiter

Log In

1

Mlops Data Engineer Jobs in Missouri (NOW HIRING)

Codinix Consulting Services

MLOps Engineer

California, MO · On-site

$120 - $150/hr

Collaborate with data engineering and platform teams to build and optimize data pipelines and ML ... Experience with Vertex AI, MLflow, Kubeflow, or similar MLOps platforms . * Familiarity with model ...

New

Boeing

Senior Data Engineer

Hazelwood, MO · On-site

$100K - $135K/yr

Senior Data Engineer Company: The Boeing Company Boeing Defense, Space & Security (BDS) is hiring a ... Experience with MLOps and MLOps tool stack such as ClearML or MLFlow * Experience working in cloud ...

Boeing

Senior Data Engineer

Hazelwood, MO

$100K - $135K/yr

Senior Data Engineer Company: The Boeing Company Boeing Defense, Space & Security (BDS) is hiring a ... Experience with MLOps and MLOps tool stack such as ClearML or MLFlow * Experience working in cloud ...

Bass Pro Shops

Lead Engineer AI/ML - Onsite

Springfield, MO · On-site

$93K - $122K/yr

This position works closely with AI leadership, Data Science, MLOps, Data Engineering, Product/Delivery, Security, Privacy, Store Operations, Merchandising, and other cross-functional partners to ...

Kohl's

$95K - $131K/yr

Collaborate with Data Scientists and Engineers across the full ML lifecycle, including building and scaling ETL pipelines, deploying models into customer-facing applications, and enabling efficient ...

Deloitte

AI Engineer Consultant

Kansas City, MO · Hybrid

... and HR data domains. * 2+ years of experience operationalizing LLMOps/MLOps capabilities ... We are hiring an AI Engineer to build and operate the data, features, and GenAI foundations that ...

Deloitte

AI Engineer Consultant

Saint Louis, MO · Hybrid

... and HR data domains. * 2+ years of experience operationalizing LLMOps/MLOps capabilities ... We are hiring an AI Engineer to build and operate the data, features, and GenAI foundations that ...

Armaninollp

Data & AI Platform Engineer

Saint Louis, MO · On-site

$111K - $133K/yr

This is an early-career engineering role focused on building, operating, and improving cloud data ... MLOps). "Armanino" is the brand name under which Armanino LLP and Armanino Advisory LLC ...

Mylitm

MLOps Engineer

California, MO · On-site

$140 - $210/hr

Autonomous Decisioning Research & Engineering About this role You will build and operate the ... What we are looking for * 4+ years building production ML or data infrastructure. * Strong Python ...

New

next page

Showing results 1-20

All JobsMlops Data Engineer JobsMlops Data Engineer Jobs in Missouri

Mlops Data Engineer information

What is an MLOps data engineer?

MLOps Data Engineers are professionals who blend expertise in machine learning (ML), operations (Ops), and data engineering to streamline the deployment and management of ML models in production environments. They design and maintain data pipelines, automate workflows, and ensure the scalability, reliability, and reproducibility of machine learning systems. Their role bridges the gap between data scientists and IT operations, enabling seamless integration of ML models into real-world applications.

What are the key skills and qualifications needed to thrive as an MLOps data engineer?

To thrive as an MLOps Data Engineer, you need a strong background in data engineering, machine learning workflows, and software development, usually supported by a degree in computer science or a related field. Expertise with cloud platforms (such as AWS, GCP, or Azure), CI/CD pipelines, containerization tools (like Docker and Kubernetes), and familiarity with orchestration frameworks are typically required, along with certifications in cloud or data engineering. Strong problem-solving abilities, collaboration, and clear communication set professionals apart in this role. These skills and qualities are critical to efficiently deploying scalable machine learning solutions and ensuring smooth collaboration between data science and engineering teams.

What are some common challenges MLOps data engineers face when deploying machine learning models into production?

MLOps Data Engineers often encounter challenges such as ensuring seamless integration between data pipelines and model serving infrastructure, managing consistent data quality, and automating model retraining and monitoring. Another common hurdle is maintaining scalability and reliability as data volumes grow, and efficiently collaborating with data scientists, software engineers, and DevOps teams. Addressing these challenges requires strong communication skills, familiarity with cloud platforms, and a proactive approach to troubleshooting and automation.

What is the difference between Mlops Data Engineer vs Data Scientist?

AspectMlops Data EngineerData Scientist
Required SkillsMachine learning deployment, cloud platforms, scripting, data pipelinesStatistical analysis, programming, data visualization, machine learning modeling
CertificationsCloud certifications, ML engineering coursesData science certifications, statistical courses
Work EnvironmentData pipelines, cloud infrastructure, ML deployment systemsData analysis, modeling, research environments
Industry UsageTech companies, AI-focused firms, cloud service providersResearch institutions, analytics firms, tech companies

The main difference between an Mlops Data Engineer and a Data Scientist lies in their focus areas. Mlops Data Engineers specialize in deploying, maintaining, and scaling machine learning models within production environments, emphasizing infrastructure and automation. Data Scientists primarily focus on analyzing data, building models, and deriving insights. Both roles require strong technical skills, but their day-to-day tasks and career paths differ significantly.

Are MLOps Data Engineers in demand?

MLOps Data Engineers are in high demand due to the increasing adoption of machine learning and AI across industries. They are skilled in deploying, managing, and maintaining ML models using tools like Docker, Kubernetes, and cloud platforms, making their expertise highly sought after in data-driven organizations.

Is MLOps required for data engineers?

MLOps is increasingly important for data engineers involved in deploying and maintaining machine learning models, as it encompasses practices like automation, monitoring, and version control. While not always mandatory, knowledge of MLOps tools such as Docker, Kubernetes, and CI/CD pipelines enhances a data engineer's ability to support scalable and reliable ML systems.

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

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

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

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

What cities in Missouri are hiring for Mlops Data Engineer jobs?

Cities in Missouri with the most Mlops Data Engineer job openings:

Mlops Data Engineer jobs near you

MLOps Engineer

Codinix Consulting Services

California, MO • On-site

$120 - $150/hr

Other

Posted yesterday

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