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Contract Model Risk Governance Jobs in Missouri (NOW HIRING)

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Contract Model Risk Governance information

What is contract model risk governance?

Contract Model Risk Governance refers to the framework and processes used by organizations to identify, assess, monitor, and mitigate risks associated with the use of models in contracts or contractual obligations. This role ensures that the use of quantitative models in financial and business contracts complies with regulatory standards and internal policies, reducing the likelihood of errors, misinterpretations, or financial losses. Professionals in this field often oversee model validation, implementation, and documentation, and work closely with compliance, risk, and legal teams. Effective governance helps maintain model integrity and supports sound decision-making across the organization.

What are the key skills and qualifications needed to thrive in contract model risk governance?

To excel in Contract Model Risk Governance, you need a strong background in risk management, quantitative analysis, and familiarity with regulatory requirements, often supported by a degree in finance, mathematics, or a related field. Proficiency with risk management software, model validation tools, and knowledge of frameworks such as SR 11-7 is typically required. Attention to detail, critical thinking, and effective communication are crucial soft skills for evaluating model risk and collaborating with stakeholders. These skills ensure robust oversight of model risk, regulatory compliance, and support sound decision-making within financial institutions.

What are some common challenges faced by professionals in contract model risk governance roles, and how can they be addressed?

Professionals in Contract Model Risk Governance often encounter challenges such as keeping up with evolving regulatory requirements, ensuring thorough model documentation, and effectively communicating risk findings to both technical and non-technical stakeholders. Balancing the need for detailed model validation with tight project timelines can also be demanding. To address these challenges, it's important to foster strong cross-functional collaboration, stay updated on industry best practices, and develop clear communication strategies for reporting risk and compliance issues.

What is the difference between Contract Model Risk Governance vs Contract Model Validation?

AspectContract Model Risk GovernanceContract Model Validation
Primary FocusOverseeing and managing risks associated with contract models, ensuring compliance and risk mitigationAssessing and testing contract models to ensure accuracy and reliability
ResponsibilitiesEstablishing policies, monitoring risk exposure, and implementing controlsPerforming independent reviews, testing model assumptions, and validating outputs
Work EnvironmentRisk management teams, compliance departments, regulatory interactionsQuantitative teams, model validation units, audit functions

While Contract Model Risk Governance focuses on managing and overseeing risks related to contract models, Contract Model Validation involves the technical assessment and testing of those models to ensure their accuracy and reliability. Both roles are essential in a comprehensive risk management framework within financial institutions and industries relying on contract models.

What are popular job titles related to Contract Model Risk Governance jobs in Missouri?

For Contract Model Risk Governance jobs in Missouri, the most frequently searched job titles are:

What job categories do people searching Contract Model Risk Governance jobs in Missouri look for?

The top searched job categories for Contract Model Risk Governance jobs in Missouri are:

What cities in Missouri are hiring for Contract Model Risk Governance jobs?

Cities in Missouri with the most Contract Model Risk Governance job openings:

Contract Model Risk Governance jobs near you
Infographic showing various Contract Model Risk Governance job openings in Missouri as of June 2026, with employment types broken down into 67% Full Time, 18% Part Time, 1% Temporary, and 14% Contract. Highlights an 84% Physical, 6% Hybrid, and 10% Remote job distribution.

MLOps Engineer - Scalable ML Pipelines & CI/CD

Codinix Consulting Services

California, MO • On-site

Other

Posted 17 days ago

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.
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