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Ai Monitoring Jobs in Indiana (NOW HIRING)

Support deployment, monitoring, maintenance, and lifecycle management of AI/ML solutions and Generative AI services. * Establish operational standards, support models, service-level objectives (SLOs ...

We deliver persistent monitoring capabilities to detect and respond to changes in any location on ... AI Solution Development & Customization * Build Retrieval-Augmented Generation (RAG) systems that ...

Establish architectural patterns for AI model deployment, monitoring, versioning, and retraining in cloud environments. * Evaluate emerging AI technologies, tools, and platforms and provide strategic ...

... monitoring, and maintenance of full-stack AI solutions. As a senior technical leader, you will leverage deep technical specialization, strategic vision, comprehensive business acumen, and exceptional ...

Establish architectural patterns for AI model deployment, monitoring, versioning, and retraining in cloud environments. * Evaluate emerging AI technologies, tools, and platforms and provide strategic ...

Monitor production agents for reliability, latency, token cost, effectiveness, usage, and safety ... applied AI/ML. * Demonstrated ability to ship at least one production-ready agentic or RAG ...

Showing results 41-60

Ai Monitoring information

What is AI monitoring?

AI monitoring refers to the process of continuously observing and analyzing artificial intelligence systems to ensure they operate as intended. This includes tracking performance, detecting anomalies, ensuring compliance with ethical guidelines, and identifying potential biases or errors. Effective AI monitoring helps organizations maintain transparency, improve system reliability, and ensure that AI models make fair and accurate decisions. It is essential in applications where AI impacts critical business or societal outcomes.

What are some common challenges faced by professionals in AI monitoring roles, and how can they be addressed?

Professionals in AI Monitoring often encounter challenges such as managing large volumes of data, identifying and responding to atypical model behavior, and ensuring compliance with ethical and regulatory standards. Staying updated on the latest AI trends and best practices, utilizing robust monitoring tools, and collaborating closely with data scientists and engineers can help address these challenges. Regular training and open communication within cross-functional teams are also essential to maintain effective oversight and quickly mitigate potential issues.

What are the key skills and qualifications needed to thrive as an AI monitoring specialist, and why are they important?

To thrive as an AI Monitoring Specialist, you need a solid understanding of data analysis, machine learning concepts, and system monitoring, often supported by a degree in computer science or a related field. Familiarity with monitoring platforms like Datadog, Prometheus, or Splunk, as well as experience with scripting languages and AI model management tools, is typically required. Attention to detail, critical thinking, and strong communication skills help specialists identify issues quickly and collaborate with technical teams. These skills and qualities are crucial for ensuring AI systems operate reliably, securely, and efficiently in real-world applications.

What is the difference between Ai Monitoring vs Data Analyst?

AspectAi MonitoringData Analyst
Required CredentialsTypically requires knowledge of AI systems, programming, and data analysis toolsRequires statistical, analytical, and data visualization skills, often with a degree in data science or related fields
Work EnvironmentOften involves monitoring AI systems in real-time, using specialized software, in tech or AI-focused companiesAnalyzes data sets, creates reports, and provides insights, working in various industries like finance, marketing, or healthcare
Employer & Industry UsageCommon in AI development firms, tech companies, and organizations deploying AI solutionsWidely used across industries for decision-making, reporting, and strategic planning

While both roles involve working with data, Ai Monitoring focuses on overseeing AI system performance and ensuring operational accuracy, whereas Data Analysts interpret data to support business decisions. Understanding these differences helps in choosing the right career path or job search focus.

Infographic showing various Ai Monitoring job openings in Indiana as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 12% Part Time, and 5% Contract. Highlights an 92% Physical, 3% Hybrid, and 5% Remote job distribution.

Artificial Intelligence (AI) Data Governance Officer

Northwest

Fishers, IN

Full-time

Posted 19 days ago


Job description

IN0534 Fishers, OH0523 Independence Bus Office, OH0713 NW Bancshares HQ, PA0258 Bellevue, PA0736 Administration Center

Job Description

The AI Governance Officer is a senior-level contributor responsible for administering the Bank's enterprise AI governance framework and supporting the oversight of artificial intelligence use cases throughout their lifecycle. The role exercises sound judgment in assessing AI-related risks, facilitating risk-based governance decisions, coordinating cross-functional review processes, and ensuring compliance with internal standards and regulatory expectations. The position plays a critical role in enabling responsible innovation while maintaining transparency, accountability, and effective oversight of AI capabilities deployed across the institution.
Essential Functions
Leads administration of the Bank's enterprise AI risk governance framework and supporting procedures. Coordinates activities across all business lines and corporate functions.
Coordinate intake, assessment, and governance activities for proposed AI use cases. Determine and document the classification of solutions as models, agents, tools, job aids, or other categories defined by policy.
Serve coordinator for the AI Working Group, including preparation of agendas, meeting materials, decision logs, and action item tracking.
Maintain the enterprise inventory of AI use cases and associated governance records.
Assess proposed AI implementations to determine applicable governance requirements and control expectations.
Develop management reporting and dashboards related to AI inventory, approvals, issues, and emerging risks. Monitor compliance with AI governance standards and escalate material issues to executive leadership.
Promote awareness and understanding of AI governance requirements throughout the institution.
Develops and maintains enterprise policies, standards, and procedures governing AI risk.
Serve as a senior member of the Model Risk team, exercising sound judgment in evaluating model risk and making decisions on model approvals and issue closures as a delegate of the Chief Model Risk Officer.
Participate in the research and evaluation of emerging modeling techniques, including AI/ML, and assess their applicability and risk implications within the institution.
Contribute to the development and enhancement of the model risk management framework, including validation methodologies, documentation standards, and governance practices.
Review academic and industry research, summarize key insights, and propose practical applications to improve model risk oversight and innovation.
Ensure compliance with Northwest's policies and procedures, as well as applicable federal and state regulations including SR26-2, interagency AI/ML guidance, and the U.S. Treasury Financial Services AI Risk Management Framework.
Interprets evolving regulatory expectations relating to artificial intelligence and translates them into practical governance requirements.
Serves as the primary coordinator for regulatory examinations, audits, and independent reviews of AI governance.
Additional Essential Functions
Ensure compliance with Northwest's policies and procedures, and Federal/State regulations
Navigate Microsoft Office Software, computer applications, and software specific to the department in order to maximize technology tools and gain efficiency
Work as part of a team
Work with on-site equipment

What You Bring to the Team

Participate in enterprise initiatives involving emerging technologies and advanced analytics.
Own the design, implementation, and enhancement of governance workflows within Archer or similar systems.
Recommend customer service enhancements
QUALIFICATIONS
To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
Education
Bachelor's Degree Undergraduate degree in risk management, business, economics, statistics, mathematics, information systems, computer science, law, or related discipline.
Master's Degree Master's degree in risk management, business, economics, statistics, mathematics, information systems, computer science, law, or related discipline.
Work Experience
8 - 12 years Experience in a financial institution or consulting environment, with demonstrated exposure to AI governanceAnd
3 - 5 years Experience evaluating or governing AI/ML use cases across the lifecycle, including:
pre-implementation assessment
control design
post-deployment monitoring
Experience with generative AI, machine learning, or decision-support systems, including understanding of associated risks (e.g., bias, explainability, data dependency, automation risk)
Experience supporting or interacting with internal audit, regulators, or independent model review functions
Experience supporting or interacting with internal audit, regulators, or independent model review functions
Additional Knowledge, Skills and Abilities
Ability to apply risk-based judgment in ambiguous situations, particularly in evaluating AI use cases where regulatory expectations and governance standards are evolving
Ability to analyze complex AI/ML concepts and communicate risks, limitations, and trade-offs clearly to both technical and non-technical stakeholders
Ability to challenge and influence business and technology stakeholders to ensure appropriate risk identification, control design, and governance outcomes
Ability to identify patterns, emerging risks, and systemic issues across multiple AI use cases rather than evaluating them in isolation.
Ability to make sound governance decisions in gray areas, including classification (model vs agent vs tool), control requirements, and escalation thresholds
Ability to coordinate cross-functional governance processes and drive clear outcomes across Risk, Technology, Compliance, Legal, and Business teams
Ability to develop and interpret risk metrics, monitoring outputs, and performance indicators for AI systems
Licenses and Certifications
Certified Information Systems Auditor (CISA)
Certified in Risk and Information Systems Control (CRISC)
Financial Risk Manager (FRM)
Certified Third-Party Risk Professional (CTPRP)
Relevant AI governance certifications

Northwest is an equal opportunity employer. We are committed to creating an inclusive environment for all employees.