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Contract Model Risk Governance Jobs in Stockbridge, GA

Learn Beyond Consulting LLC

AI Devops Engineer

Atlanta, GA · On-site

$50.75 - $69.50/hr

Atlanta, GA 5 Month Contract Only W2 3 days hybrid onsite in Atlanta Top Skills'' Details Cloud ... model risk management controls, including auditability, explainability, and access governance.

AIG

AI Model Validation Lead, GenAI

Atlanta, GA · On-site

$210K - $280K/yr

... risk and serve our customers. This team is central to our vision of the future and the core of our ... Strong knowledge of data governance, ethical AI principles, and regulatory requirements impacting ...

YouLend

Credit Risk Lead

Atlanta, GA · On-site

Governance, Compliance and Quality Assurance * Own and maintain the Risk Governance Framework ... Strong understanding of credit underwriting principles, data science model application, risk ...

Visa

Director, VCA NA Risk Practice

Atlanta, GA · On-site

Develop customer lifetime value (CLV) and portfolio economics models to inform acquisition ... Collaborate crossfunctionally with Product, Engineering, Risk Governance, Legal, Compliance, and ...

Novelis

AI Governance Principal

Atlanta, GA · On-site

Ensure every new AI initiative undergoes comprehensive evaluation across model selection, security and data risk review, data quality assessment, and governance compliance before proceeding to ...

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Showing results 21-40

All JobsContract Model Risk Governance JobsContract Model Risk Governance Jobs in Stockbridge, GA

Contract Model Risk Governance information

See Stockbridge, GA salary details

$8

$38

$121

How much do contract model risk governance jobs pay per hour?

As of Aug 6, 2026, the average hourly pay for contract model risk governance in Stockbridge, GA is $38.98, according to ZipRecruiter salary data. Most workers in this role earn between $12.69 and $61.49 per hour, depending on experience, location, and employer.

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 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 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 popular job titles related to Contract Model Risk Governance jobs in Stockbridge, GA? For Contract Model Risk Governance jobs in Stockbridge, GA, the most frequently searched job titles are:
What cities near Stockbridge, GA are hiring for Contract Model Risk Governance jobs? Cities near Stockbridge, GA with the most Contract Model Risk Governance job openings:
Contract Model Risk Governance jobs near you

AI Devops Engineer

Learn Beyond Consulting LLC

Atlanta, GA • On-site

$50.75 - $69.50/hr

Other

Posted 15 days ago

Job description

Title : AI Devops Engineer

Location: Atlanta, GA

5 Month Contract

Only W2 

3 days hybrid onsite in Atlanta

Top Skills'' Details

Cloud Architecture & Automation: 10+ years of experience designing and deploying cloud infrastructure using Google Cloud Platform (preferred), Azure, or Databricks with strong Terraform/Bicep expertise.

DevSecOps & Governance: Proven ability to implement secure CI/CD pipelines, cloud security controls (IAM, encryption, secrets management), and governance frameworks.

AI/ML Operations & Observability: Hands-on experience supporting production AI/ML environments with model monitoring, drift detection, logging, alerting, and observability solutions

Job Description

We are seeking a Senior DevOps Engineer IV to support the design, implementation, and optimization of enterprise AI platforms and cloud infrastructure. This role will focus on cloud architecture, Infrastructure as Code (IaC), AI/ML operations, observability, security, governance, and Agentic AI systems. The ideal candidate will have extensive experience building scalable cloud environments, implementing DevOps best practices, and enabling production-grade AI solutions in regulated enterprise environments.

Key Responsibilities

Design, deploy, and maintain enterprise cloud infrastructure supporting AI/ML workloads.

Implement Infrastructure as Code using Terraform, Bicep, or similar automation tools.

Develop and manage CI/CD pipelines with integrated security, governance, and compliance controls.

Architect scalable, highly available AI platforms across cloud environments.

Drive FinOps practices, including cloud cost optimization, resource utilization, and enterprise cost visibility.

Implement AI observability frameworks covering model performance, drift detection, reliability, business KPIs, and operational monitoring.

Design and support AI/ML lifecycle management including monitoring, retraining strategies, logging, alerting, and incident response.

Embed security, compliance, and model risk management controls into AI development and deployment processes.

Support development and operationalization of Agentic AI solutions, including orchestration, monitoring, testing, and governance.

Establish best practices for AgentOps, model governance, and AI platform reliability

Additional Skills & Qualifications

Design and implementation of multi-cloud AI infrastructure with integrated governance and policy controls.

Experience embedding security, compliance, and governance controls directly into IaC and deployment pipelines.

Strong understanding of AI FinOps, including token optimization, cost-performance tradeoffs, and enterprise cost visibility.

Experience implementing model risk management controls, including auditability, explainability, and access governance.

Knowledge of designing AI systems for regulated environments and enforcing runtime guardrails and policy controls.

Ability to develop enterprise-wide AI observability strategies, covering model performance, data drift, bias detection, reliability, and business KPIs.

Experience implementing centralized monitoring frameworks and automated response mechanisms across AI platforms.

Exposure to LLM-based applications, AI agents, prompt engineering, API integrations, and orchestration frameworks such as LangChain.

Experience designing and supporting agent-based systems at scale, including multi-agent coordination, tool orchestration, memory management, and state management.

Knowledge of AgentOps practices, including AI deployment, testing, monitoring, iteration, and governance.

Understanding of autonomous AI system failure modes and mitigation strategies.

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