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Model Risk Jobs in Tampa, FL (NOW HIRING)

Nous Infosystems

AI Solution Architect

Tampa, FL · On-site

$57.25 - $75.50/hr

Experience with AI governance, model risk management, and responsible AI practices (fairness, explainability, security, and privacy). * Familiarity with vector databases, semantic search, RAG ...

Deloitte

Senior Forward Deployed Engineer- AWS

Tampa, FL · On-site

$98K - $135K/yr

Apply architecture decisions that balance quality, safety, latency, cost, and model risk. Deliver production-quality code using strong practices in testing, CI/CD, logging, versioning, and ...

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All JobsModel Risk JobsModel Risk Jobs in Tampa, FL

Model Risk information

See Tampa, FL salary details

$13

$28

$69

How much do model risk jobs pay per hour?

As of Jul 18, 2026, the average hourly pay for model risk in Tampa, FL is $28.67, according to ZipRecruiter salary data. Most workers in this role earn between $18.41 and $36.59 per hour, depending on experience, location, and employer.

What are some typical challenges faced by professionals working in Model Risk, and how can they be addressed?

Professionals in Model Risk often encounter challenges such as ensuring model accuracy, managing regulatory compliance, and effectively communicating complex technical findings to non-technical stakeholders. Addressing these challenges requires a strong understanding of both quantitative modeling and relevant regulations, as well as strong collaboration skills to work with model developers, auditors, and business units. Staying informed about evolving regulatory standards and participating in ongoing training can also help model risk professionals remain effective and add value to their organizations.

What are the key skills and qualifications needed to thrive as a Model Risk Analyst, and why are they important?

To thrive as a Model Risk Analyst, you need a solid background in quantitative analysis, statistics, or finance, often supported by an advanced degree in a related field. Familiarity with model validation tools, programming languages such as Python or R, and regulatory frameworks like SR 11-7 is essential. Strong analytical thinking, attention to detail, and effective communication skills are crucial for evaluating models and presenting findings to stakeholders. These skills ensure model integrity, regulatory compliance, and risk mitigation in financial institutions.

What is the difference between Model Risk vs Model Validation?

AspectModel RiskModel Validation
Primary FocusIdentifying, assessing, and mitigating risks associated with modelsEvaluating and testing models to ensure accuracy and reliability
Required CredentialsQuantitative skills, risk management certifications, industry experienceQuantitative expertise, validation certifications, industry knowledge
Work EnvironmentRisk management teams within financial institutions or firmsModel validation teams, often within risk or model development departments
Industry UsageUsed across banking, insurance, and investment firms to manage model-related risksCommonly employed in financial services to verify model performance

Model Risk focuses on managing the potential negative impacts of models, including errors and misuse, while Model Validation concentrates on testing and confirming the accuracy and robustness of models. Both roles are essential in financial industries to ensure models are reliable and risks are minimized.

What is model risk?

Model risk refers to the potential for adverse consequences resulting from decisions based on incorrect or misused models. In financial institutions, model risk can arise if a model's assumptions are flawed, if the data input is poor, or if the model is applied inappropriately. Managing model risk involves validating models, monitoring their performance, and ensuring that they are used within their intended scope. Effective model risk management helps organizations avoid significant financial losses and comply with regulatory requirements.
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What job categories do people searching Model Risk jobs in Tampa, FL look for? The top searched job categories for Model Risk jobs in Tampa, FL are:
Model Risk jobs near you
AI Solution Architect

AI Solution Architect

Nous Infosystems

Tampa, FL • On-site

$57.25 - $75.50/hr

Other

Posted 11 days ago

Job description

Role: AI Solution Architect

Location: Onsite – Tampa, Florida

Experience: 8–15 years of overall IT experience, with at least 3–5 years focused on AI solution architecture and delivery. 

Mandatory Skill Tags: AI Solution Architecture, AI solution design, latest AI models, LLMs, enterprise AI architecture, cloud AI/ML platforms, data & MLOps integration 

Secondary Skill Tags: responsible AI, AI governance, vector databases, RAG, semantic search, MLOps tools, cloud-native architecture, microservices, Kubernetes, agile delivery 

Job Summary:

The Onsite AI Solution Architect will lead the end-to-end architecture, design, and implementation of AI and AI-native solutions for Advantive. This role will closely collaborate with business stakeholders, product owners, data teams, and engineering to translate business requirements into scalable, secure, and robust AI architectures. The architect will provide thought leadership on latest AI models and LLMs and ensure best practices, governance, and standards are adopted across AI initiatives.  

Key Responsibilities:

  • Lead the architecture, design, and technical roadmap for AI and AI-native solutions aligned to business strategy. 
  • Translate business and functional requirements into scalable AI solution architectures, covering data, model, application, and integration layers. 
  • Evaluate, select, and integrate latest AI models and LLMs (including cloud and third-party services) into enterprise applications and workflows. 
  • Define reference architectures, patterns, standards, and reusable components for AI solution delivery across the organization. 
  • Collaborate with data engineers, MLOps engineers, application developers, and product teams to ensure high-quality, production-grade AI deployments. 
  • Establish non-functional requirements (performance, security, reliability, observability) and ensure AI solutions meet enterprise architecture and compliance guidelines. 
  • Conduct technical reviews, PoCs, and feasibility assessments for new AI use cases and guide teams on best practices and optimization. 
  • Provide architectural leadership, mentoring, and guidance to project teams, driving continuous improvement and innovation in AI solution delivery.  

Required Skills:

  • Strong experience in AI Solution Architecture, designing and delivering enterprise-grade AI solutions. 
  • Proven expertise in architectural design involving AI solutions, including end-to-end solution blueprints and reference architectures.
  • Hands-on knowledge of designing AI-based solutions using machine learning, deep learning, and LLM-based approaches. 
  • In-depth understanding of latest AI models and large language models (LLMs), including their capabilities, limitations, and suitable use cases. 
  • Experience with AI/ML platforms and services (e.g., Azure AI, AWS AI/ML, Google Cloud AI, or equivalent). 
  • Solid understanding of data architecture concepts, including data pipelines, feature stores, model deployment, and monitoring (MLOps). 
  • Strong background in application integration patterns (APIs, microservices, event-driven architecture) for embedding AI into products and workflows. 
  • Ability to create high-quality architectural artifacts (HLDs, LLDs, sequence diagrams, data flow diagrams) and communicate them to technical and non-technical stakeholders. 
  • Strong stakeholder management, communication, and leadership skills to drive consensus and decision-making.  

Good to Have Skills

  • Experience with AI governance, model risk management, and responsible AI practices (fairness, explainability, security, and privacy). 
  • Familiarity with vector databases, semantic search, RAG (Retrieval-Augmented Generation), and knowledge-graph-based solutions. 
  • Exposure to MLOps tools and frameworks for CI/CD of ML models and LLM-based applications. 
  • Experience in designing multi-tenant, cloud-native architectures using containers and orchestration (Docker, Kubernetes). 
  • Knowledge of enterprise integration with ERP/CRM/line-of-business applications. 
  • Prior experience in leading AI architecture for product-based or ISV organizations. 
  • Experience working in agile delivery environments and collaborating with distributed teams.  

Educational Qualification

Bachelor’s or Master’s degree in Computer Science, Information Technology, Engineering, or a related discipline from a recognized institution. 

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