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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 Engineer Principal within PNC's Data and Automation Technology organization, you will be based in Pittsburgh, PA, Strongsville, OH or Dallas, TX.
We are looking for a MLOPs Quality Engineer Principal to join the Data \& Automation organization, supporting the MLOps ecosystem. In this hands on individual contributor role, you will be responsible for driving scalable, automation\-first validation strategies across teams, systems, and complex integrations. This role operates at both hands\-on and strategic levels, influencing best practices, improving testability, and establishing reusable quality patterns that enable both design \& operating effectiveness. You will be responsible for designing, building, coordinating, executing \& validating risk \& control testing strategies across the MLOPs portfolio.
You will work closely with QE resources, Development teams, and Platform Engineers to strengthen \& standardize test coverage, automation, and end to end quality w/ evidence.
Key Responsibilities:
- Design MLOps testing strategies which verify that the rules, policies, and procedures put in place as internal controls are properly designed and operating effectively
- Champion automation, continuous testing and shift\-left testing methodologies
- Build and maintain test automation and ensure stds are adopted and followed.
- Identify controls gaps and develop test harnesses to detect and validate.
- Support test data and test environment readiness across environments.
- Analyze defect trends and production incidents — contributing to root cause analysis and recommending preventive measures.
- Provide quality sign off as needed
- Contribute to QE standards and best practices for MLOps testing techniques, tools, and approaches within the ecosystem.
Key Skills \& Qualifications:
- Strong understanding of the MLOps lifecycle, including model validation, deployment, and monitoring
- Experience designing scalable test strategies across complex data and ML systems
- Expertise in test automation and continuous testing within CI/CD pipelines
- Proficiency in Python, API testing, and data validation (SQL)
- Knowledge of cloud platforms (AWS, Azure, or GCP) and distributed data systems
- Experience with risk\-based testing, controls validation, and audit readiness
- Ability to analyze defects and production issues and drive root cause solutions
- Strong collaboration and stakeholder influence across engineering and QE teams
- Self\-driven with a strategic mindset and hands\-on execution capabilities
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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- Drives the development of test strategies and plans, ensuring alignment with enterprise\-level objectives and delivery timelines.
- Escalates systemic issues to Quality Engineer leadership and influences resolution strategies.
- Oversees risk and defect processes, ensuring adherence to ECO standards. Provides detailed insights through accurate and timely reporting.
- Leads preparation and delivery of test summary reports and documentation for Technology leaders, Governance/Risk teams, and business stakeholders.
- Collaborates across teams and participates in Quality Engineer CoP meetings to support continuous improvement and knowledge sharing.
- Provides strategic mentorship and oversight to more junior Quality Engineer team members, ensuring consistent execution of testing standards.
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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Competitive Advantages, Customer Solutions, Design, Enterprise Architecture Framework, Machine Learning (ML), Machine Learning Operations, Model Validation, QA Automation, Risk Assessments, Technical Knowledge, Test Automation StrategyCompetencies
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Application Testing, Coaching Others, Influencing, Process Management, Software Development Life Cycle, Software Quality Assurance And Testing, System Testing, Technical Documentation Management, Technical TroubleshootingWork Experience
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Roles at this level typically require a university / college degree, with 5\+ years of industry\-relevant experience. Specific certifications are often required. 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: $91,000\.00 – $185,900\.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 08/11/2026, 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 [email protected]. 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 $91K-$185K range is below the median for MLOps Engineer roles in our dataset (median: $168K across 34 roles with salary data).
View full MLOps Engineer salary data →Role Details
About This Role
MLOps Engineers build the infrastructure that keeps ML models running in production. They own CI/CD pipelines for model deployment, monitoring for data drift and model degradation, and the tooling that lets data scientists ship faster. If ML Engineers build the models, MLOps Engineers build the roads those models travel on.
The job is fundamentally about reliability and velocity. Data scientists want to iterate fast. Product teams want stable predictions. Your job is to make both happen simultaneously. That means building deployment pipelines that catch regressions before they hit production, monitoring systems that alert on data drift before it degrades model performance, and self-service tooling that lets data scientists deploy without filing a ticket.
Across the 4,317 AI roles we're tracking, MLOps Engineer positions make up 1% of the market. At PNC Financial Services Group, this role fits into their broader AI and engineering organization.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
What the Work Looks Like
A typical week involves: debugging a model deployment that's serving stale predictions, building a new monitoring dashboard for a feature team, writing Terraform for GPU-enabled inference clusters, reviewing pull requests for the ML platform's CI/CD pipeline, and meeting with data scientists to understand their pain points. You're the bridge between ML and infrastructure.
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
Skills Required
Kubernetes, Docker, and cloud infrastructure are baseline. Most roles want experience with ML-specific tooling: MLflow, Kubeflow, Weights & Biases, or similar. Strong DevOps fundamentals matter more than ML theory. You need to understand model serving (TorchServe, Triton, vLLM), monitoring (Prometheus, Grafana), and infrastructure-as-code (Terraform, Pulumi).
GPU infrastructure knowledge is increasingly valuable as LLM inference becomes a major cost center. Understanding GPU scheduling, multi-node training setups, and inference optimization (quantization, batching, caching) puts you in the top tier. Experience with model registries and feature stores rounds out the profile.
Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
Compensation Benchmarks
MLOps Engineer roles pay a median of $203,000 based on 85 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($138K) sits 32% below the category median. Disclosed range: $91K to $185K.
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.
PNC Financial Services Group AI Hiring
PNC Financial Services Group has 5 open AI roles right now. They're hiring across AI Software Engineer, MLOps Engineer, Data Scientist. Based in Pittsburgh, PA, US. Compensation range: $143K - $185K.
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 MLOps Engineer roles include DevOps Engineer, Platform Engineer, Data Engineer.
From here, career progression typically leads toward ML Platform Lead, Infrastructure Architect, Engineering Manager.
DevOps engineers with ML curiosity have the shortest path. You already understand deployment, monitoring, and infrastructure. Add ML-specific knowledge (model serving, data pipelines, experiment tracking) and you're competitive. The career ceiling is high: ML Platform Lead roles at top companies pay well because the infrastructure complexity is enormous.
What to Expect in Interviews
Interviews emphasize infrastructure and reliability. Expect questions about CI/CD for ML models, monitoring for data drift, and how you'd design a model serving platform that handles 10K requests per second. Coding rounds focus on Python and infrastructure-as-code (Terraform, Helm). Be ready to discuss tradeoffs between different model serving frameworks and how you'd handle rollback when a new model degrades performance.
When evaluating opportunities: Good MLOps postings specify their ML stack, infrastructure scale, and the problems they're solving (deployment velocity, cost optimization, monitoring gaps). Red flag: companies that want MLOps but don't have any models in production yet. You'll end up doing general DevOps instead.
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).
MLOps demand tracks closely with production ML adoption. As more companies move models from notebooks to production, the need for MLOps grows. The role is well-established at large tech companies and growing fast at mid-stage startups that are hitting the 'our models work in notebooks but break in production' phase.
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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