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Ai Risk Manager Jobs in Kentucky (NOW HIRING)

AI Center of Excellence Director

Louisville, KY · On-site

$45K - $62K/yr

... managing an inherited capability, but building teams, governance models and production workflows from a blank slate. * Working knowledge of AI governance frameworks including risk tiering, use case ...

Showing results 21-40

Ai Risk Manager information

How much do AI risk managers make?

AI risk managers typically earn between $100,000 and $180,000 annually, depending on experience, education, and location. Senior roles or those in high-demand industries can offer higher salaries, often supplemented with bonuses and benefits. Strong knowledge of AI systems, risk assessment, and compliance are key skills for this role.

What does an AI risk manager do?

An AI risk manager assesses and mitigates potential risks associated with artificial intelligence systems, including ethical, safety, and compliance concerns. They develop strategies to ensure AI models operate reliably and responsibly, often working with data scientists and engineers to implement risk management frameworks and monitor AI performance. Strong analytical skills and knowledge of AI ethics, regulations, and tools are essential for this role.

What is the difference between Ai Risk Manager vs Data Scientist?

AspectAi Risk ManagerData Scientist
Required CredentialsTypically requires a degree in risk management, AI, or related fields; certifications in AI or risk management are commonRequires a degree in computer science, statistics, or related fields; certifications in data analysis or machine learning are common
Work EnvironmentWorks in financial, insurance, or tech industries focusing on AI risk assessment and mitigationWorks across industries analyzing data, building models, and deriving insights
Employer & Industry UsageUsed by organizations managing AI deployment risks, especially in regulated sectorsUsed by companies developing AI solutions, data-driven products, and analytics teams

The main difference is that an Ai Risk Manager focuses on identifying and mitigating risks associated with AI systems, often requiring knowledge of risk management and AI ethics. In contrast, a Data Scientist primarily analyzes data and builds models to extract insights, with less emphasis on risk mitigation. Both roles may overlap in AI projects but serve distinct functions within organizations.

Is AI going to replace AI Risk Manager jobs?

AI Risk Managers analyze and mitigate risks associated with artificial intelligence systems, a role that requires specialized knowledge of AI technologies, ethics, and compliance. While AI tools can assist in risk assessment, the job involves strategic decision-making and oversight that are unlikely to be fully automated in the near future.
What are popular job titles related to Ai Risk Manager jobs in Kentucky? For Ai Risk Manager jobs in Kentucky, the most frequently searched job titles are:
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What cities in Kentucky are hiring for Ai Risk Manager jobs? Cities in Kentucky with the most Ai Risk Manager job openings:
Infographic showing various Ai Risk Manager job openings in Kentucky as of August 2026, with employment types broken down into 85% Full Time, 12% Part Time, and 3% Contract. Highlights an 84% Physical, 3% Hybrid, and 13% Remote job distribution.

Full-time

Posted 18 days ago


Job description

Job title: AI Product Owner
Location :Onsite in Louisville, KY
Duration: 8+Months
Job Description:
Product Strategy & Vision
• Define and own the product vision, strategy, and roadmap for agentic AI solutions aligned with business objectives
• Identify high-value use cases where autonomous agents can drive efficiency, automation, or new capabilities
• Evaluate build-vs-buy decisions across agentic AI frameworks (LangChain, LangGraph, AutoGen, CrewAI) and LLM providers
• Stay current on the evolving agentic AI landscape and translate emerging capabilities into product opportunities
Backlog & Requirements Management
• Own and prioritize the product backlog, translating business requirements into clear user stories and acceptance criteria for engineering teams
• Define agent behaviors, decision boundaries, and success criteria (task completion rates, accuracy, latency, cost per task)
• Specify requirements for agent memory, tool access, multi-agent orchestration, and human-in-the-loop checkpoints
• Balance autonomy vs. control - determining where agents should act independently vs. escalate to humans
Cross-Functional Collaboration
• Act as the primary liaison between business stakeholders, AI/ML engineering teams, UX designers, and data governance/compliance teams
• Partner with engineering to scope technical feasibility, effort estimation, and architecture trade-offs for agentic workflows
• Work with data science/ML teams to define evaluation frameworks for hallucination rates, task success, and agent reliability
• Collaborate with design teams on human-agent interaction patterns and interfaces (chat, dashboards, approval workflows)
Governance, Risk & Compliance
• Define guardrails, escalation paths, and fallback mechanisms for agent failures or edge cases
• Ensure agentic systems meet responsible AI standards: transparency, auditability, bias mitigation, and data privacy
• Partner with legal/compliance teams on risk assessment for autonomous decision-making systems
• Establish monitoring and audit trails for agent actions, especially in regulated domains
Launch & Performance Management
• Drive go-to-market planning, pilot programs, and phased rollouts of agentic AI features
• Define and track KPIs/OKRs: task automation rate, agent accuracy, cost savings, user adoption, time-to-resolution
• Analyze production agent performance data to identify improvement opportunities and prioritize iterations
• Manage stakeholder communication, demos, and executive reporting on agentic AI product progress
Agile Delivery
• Run sprint planning, backlog grooming, and stakeholder demos in an Agile/Scrum environment
• Prioritize technical debt, model updates, and framework migrations alongside feature development
• Manage dependencies across multiple agent workstreams and cross-functional teams
Required Skills:
1. Health Care Products , Consumer care product develop , Dx Product Lifecycle Management
2. Proven experience as a Product Owner/Product Manager, ideally on AI/ML or automation products
3. Trizetto Products
Years of Experience: 12.00 Years of Experience
Regards
surya
surya@rurisoft.com