Interested in this AI/ML Engineer role at PNC Financial Services Group?
Apply Now →About This Role
Job ProfilePosition Overview
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At PNC, our people are our greatest differentiator and competitive advantage in the markets we serve. We are all united in delivering the best experience for our customers. We work together each day to foster an inclusive workplace culture where all of our employees feel respected, valued and have an opportunity to contribute to the company’s success. As a Quality Assurance Specialist Sr within PNC's Technology organization, you will be based in Pittsburgh, PA, Cleveland, OH, Dallas, TX, or Birmingham AL.
The Senior Quality Assurance Specialist supports the Engagement \& Collaboration team within Solution Delivery Enablement, a highly collaborative, enterprise\-facing group focused on elevating testing quality, consistency, tooling adoption, and process adherence across multiple lines of business.
This role goes beyond traditional QA execution. It combines hands\-on testing expertise with stakeholder enablement, documentation, training, and process support in a governed enterprise environment. The ideal candidate will act as a key contributor who can bridge testing execution, enablement, and stakeholder support.
Key Responsibilities:
- Participate in test planning, test case design, execution, and defect management
- Support UAT, SIT, and regression testing activities
- Execute and validate test scenarios and ensure accurate defect tracking and reporting
- Ensure proper documentation and traceability of test results and outcomes
- Provide guidance and support for testing tools and frameworks (e.g., Jira/Xray or similar)
- Assist teams with structured testing processes and artifacts, including:
- Test Strategy \& Approach (TSA/MTSA)
- Test Summary Reports (TSR)
- Troubleshoot tool\-related and process\-related issues via support channels, chats, and office hours
- Promote consistent usage of testing processes, workflows, and standards
- Develop and maintain Confluence content, job aids, and training materials
- Contribute to onboarding resources and knowledge\-sharing initiatives
- Continuously enhance guidance based on common user needs and feedback
- Support Communities of Practice and training/enablement sessions
- Provide support to internal users via office hours, forums, and ticketing channels
- Partner with QE team members, developers, and business stakeholders
- Help teams adopt and adhere to testing best practices and standards
- Participate in cross\-team meetings, reviews, and knowledge\-sharing forums
- Contribute to strategic efforts such as:
- QE refresher training and onboarding content
- MTSA and Xray\-related initiatives
- UAT readiness and execution support
- Testing guidance updates and process improvements
- Tooling and communication rollouts
- Provide support for urgent testing/process needs and workaround solutions
- Participate in testing and user acceptance validation of AI\-enabled and GenAI\-enabled capabilities, including chatbot, copilot, agent\-based, and other intelligent workflow solutions
- Work with pilot user groups, business stakeholders, and operational teams (including pilot crew populations where applicable) to validate agent functionality, usability, and business outcomes
- Design and execute test scenarios that evaluate AI\-generated outputs against source data, business requirements, expected behaviors, and governance standards
- Identify and document risks, defects, and observations related to AI capabilities, including hallucinations, inaccurate responses, inconsistent outputs, privacy concerns, sensitive data exposure, and prompt injection/security risks
- Support documentation of AI/GenAI testing approaches, evaluation criteria, evidence collection, and test results to ensure traceability and auditability
Key Skills \& Qualifications:
- Strong experience in quality engineering / quality assurance and hands\-on testing
- Exposure to testing AI\-enabled and Generative AI\-enabled functionality in an enterprise environment
- Familiarity with AI\-specific testing considerations such as:
- Prompt testing and prompt variation analysis
- Hallucination identification and risk assessment
- Privacy, sensitive data handling, and responsible AI considerations
- Prompt injection and other AI\-related security risks
- Output evaluation methodologies and evidence documentation
- Demonstrated expertise in:
- Test planning and execution
- Test case creation and defect management
- Supporting testing tools and processes
- Experience working in a governed enterprise environment
- Excellent communication, documentation, and stakeholder support skills
- Ability to balance execution, enablement, and user support responsibilities
- Experience with Jira, Xray, or similar test management tools
- Understanding of structured testing artifacts:
- Test Strategy \& Approach (TSA/MTSA)
- Test Summary Reports (TSR)
- Familiarity with:
- Confluence and SharePoint
- Test automation frameworks (e.g., Selenium, Cucumber)
- Broader QE/DevOps tool ecosystem
PNC is an in\-office company that fosters a supportive culture where employees can thrive and achieve balance. We encourage candidates to connect with their recruiter and hiring manager to understand workplace expectations and ensure the role aligns with their goals.
PNC will not provide sponsorship for employment visas or participate in STEM OPT for this position.Job Description
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- Leads and reports on reviews and tests of software systems and products to verify compliance with applicable specifications and standards.
- Leads the effort to work with software developers to resolves issues of incompliance through system modification, reconfiguration or workarounds. Establishes objectives for testing cycles. Develops test scenarios for unit, process, function, integration and acceptance testing .
- Leads the team in planning and estimating quality assurance support requirements for new products, new functions and new components. Leads audits and reviews .
- Oversees and leads the efforts to monitor adherence to quality standards in the development, implementation and upkeep of software products. Monitors effectiveness of the assurance process and tools .
- Oversees the tracking of quality assurance metrics such as defect density and open defect counts. Oversees team members responsible for testing and inspection of products to determine compliance with specifications .
PNC Employees take pride in our reputation and to continue building upon that we expect our employees to be:
- Customer Focused \- Knowledgeable of the values and practices that align customer needs and satisfaction as primary considerations in all business decisions and able to leverage that information in creating customized customer solutions.
- Managing Risk \- Assessing and effectively managing all of the risks associated with their business objectives and activities to ensure they adhere to and support PNC's Enterprise Risk Management Framework.
Qualifications
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Successful candidates must demonstrate appropriate knowledge, skills, and abilities for a role. Listed below are skills, competencies, work experience, education, and required certifications/licensures needed to be successful in this position.
Preferred Skills
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Analytical Thinking, Controls Testing, Corporate Governance, Generative AI, Quality Assurance (QA), Quality Support, Software Testing, System Integration Testing (SIT), Test Case Development, Test Case Planning, Test Planning, Usability Testing, User Acceptance Testing (UAT)Competencies
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Accuracy and Attention to Detail, Analytical Thinking, Influencing, Process Management, Products and Services, Software Development Life Cycle, Software Quality Assurance And Testing, Technical Documentation ManagementWork Experience
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Roles at this level typically require a university / college degree, with 3\+ years of relevant / direct industry experience. Certifications are often desired. In lieu of a degree, a comparable combination of education, job specific certification(s), and experience (including military service) may be considered.Education
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BachelorsCertifications
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No Required Certification(s)Licenses
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No Required License(s)Pay Transparency
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Base Salary: $52,500\.00 – $127,500\.00
Salaries may vary based on geographic location, market data and on individual skills, experience, and education. This role is incentive eligible with the payment based upon company, business and/or individual performance.Application Window
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Generally, this opening is expected to be posted for two business days from 07/10/2026, although it may be longer with business discretion.Benefits
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PNC offers a comprehensive range of benefits to help meet your needs now and in the future. Depending on your eligibility, options for full\-time employees include: medical/prescription drug coverage (with a Health Savings Account feature), dental and vision options; employee and spouse/child life insurance; short and long\-term disability protection; 401(k) with PNC match, pension and stock purchase plans; dependent care reimbursement account; back\-up child/elder care; adoption, surrogacy, and doula reimbursement; educational assistance, including select programs fully paid; a robust wellness program with financial incentives.
In addition, PNC generally provides the following paid time off, depending on your eligibility: maternity and/or parental leave; up to 11 paid holidays each year; 9 occasional absence days each year, unless otherwise required by law; between 15 to 25 vacation days each year, depending on career level; and years of service.
To learn more about these and other programs, including benefits for full time and part\-time employees, visit pncthrive.com.
Disability Accommodations Statement
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If an accommodation is required to participate in the application process, please contact us via email at AccommodationRequest@pnc.com. Please include “accommodation request” in the subject line title and be sure to include your name, the job ID, and your preferred method of contact in the body of the email. Emails not related to accommodation requests will not receive responses. Applicants may also call 877\-968\-7762 and say "Workday" for accommodation assistance. All information provided will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.
At PNC we foster an inclusive and accessible workplace. We provide reasonable accommodations to employment applicants and qualified individuals with a disability who need an accommodation to perform the essential functions of their positions.
Equal Employment Opportunity (EEO)
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PNC provides equal employment opportunity to qualified persons regardless of race, color, sex, religion, national origin, age, sexual orientation, gender identity, disability, veteran status, or other categories protected by law.
This position is subject to the requirements of Section 19 of the Federal Deposit Insurance Act (FDIA) and, for any registered role, the Secure and Fair Enforcement for Mortgage Licensing Act of 2008 (SAFE Act) and/or the Financial Industry Regulatory Authority (FINRA), which prohibit the hiring of individuals with certain criminal history.
California Residents
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Refer to the California Consumer Privacy Act Privacy Notice to gain understanding of how PNC may use or disclose your personal information in our hiring practices.
Salary Context
This $52K-$127K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At PNC Financial Services Group, 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 $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($90K) sits 59% below the category median. Disclosed range: $52K to $127K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
PNC Financial Services Group AI Hiring
PNC Financial Services Group has 3 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in Pittsburgh, PA, US. Compensation range: $127K - $202K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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