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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 governance And
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.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Northwest Bank, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Northwest Bank AI Hiring
Northwest Bank has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Fishers, IN, US, Columbus, OH, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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