Interested in this AI/ML Engineer role at PNC Financial Services Group?
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Job ProfilePosition Overview
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 Software Engineering Manager within PNC's Technology organization, you will be based in Pittsburgh, PA.
PNC is seeking a Software Engineering Group Manager to lead a large-scale AI Engineering organization responsible for designing, building, and scaling enterprise Artificial Intelligence solutions that drive innovation across the bank. This leader will oversee an engineering team focused on advancing AI platforms, frameworks, and development patterns that enable future AI-powered business capabilities.
The ideal candidate brings a unique blend of technical depth, AI engineering expertise, and proven people leadership. This individual will lead a team of highly skilled engineers who drive architecture and innovation efforts across the organization. The role requires a leader who can establish engineering strategy, influence technology direction, and enable teams to deliver scalable, secure, and production-ready AI solutions.
Key Responsibilities:
- Leadership & Organizational Management
- Build a high-performing engineering culture focused on velocity, innovation, accountability, collaboration, and continuous learning.
- Inspire and retain top AI and software engineering talent while creating clear career progression opportunities.
- Establish organizational goals, workforce planning strategies, and talent development programs aligned to business priorities.
- Partner with senior technology leaders to define long-term engineering strategy and investment priorities.
- Create an environment where senior technical leaders can effectively drive innovation while maintaining delivery excellence.
- Manage capacity planning, hiring, budgeting, and resource allocation across multiple initiatives and workstreams.
- AI Engineering Strategy & Technical Leadership
- Define and execute the AI engineering roadmap, identifying technologies, frameworks, and tooling that enhance current and future AI capabilities.
- Evaluate emerging AI technologies and engineering patterns to drive innovation and improve developer productivity.
- Provide technical oversight for the design, development, and deployment of enterprise-scale AI solutions.
- Guide architectural decisions across AI applications, machine learning platforms, generative AI solutions, and intelligent automation initiatives.
- Partner with senior engineers and architects to establish engineering standards, reusable frameworks, and scalable development practices.
- Ensure AI solutions are built with security, resiliency, observability, and governance requirements embedded from inception.
- Maintain sufficient hands-on knowledge of AI engineering trends, platforms, and tools to effectively lead technical discussions and decision-making.
- Software Engineering Delivery
- Lead the successful delivery of AI-powered products and platform capabilities from concept through production.
- Establish and enforce software development lifecycle (SDLC) best practices, including code quality, automated testing, CI/CD, and release management.
- Drive adoption of modern engineering practices, cloud-native architectures, and platform engineering capabilities.
- Partner with product, architecture, data, and infrastructure teams to deliver scalable, enterprise-grade solutions.
- Ensure engineering teams meet commitments for quality, performance, availability, and security.
- Stakeholder Partnership & Influence
- Collaborate with executive and business stakeholders to identify opportunities where AI can drive measurable business outcomes.
- Translate strategic business objectives into technology initiatives and engineering priorities.
- Communicate engineering strategy, delivery progress, risks, and investment recommendations to senior leadership.
- Influence enterprise technology direction and advocate for AI engineering best practices across the organization.
Key Skills & Qualifications:
- Progressive software engineering, data engineering, or technology leadership experience.
- Experience leading large-scale engineering teams and managing technical leaders.
- Proven experience building, scaling, and leading high-performing engineering organizations.
- Demonstrated experience delivering AI, machine learning, or Generative AI solutions in enterprise environments.
- Strong understanding of modern software engineering principles, architecture patterns, and cloud-native development.
- Ability to balance strategic leadership responsibilities with technical oversight and architectural guidance.
- Exceptional communication, stakeholder management, and executive presence skills.
- Experience leading cross-functional teams through technology transformation and innovation initiatives.
- Banking or financial services industry experience preferred.
- Familiarity with regulatory, governance, and risk management requirements within highly regulated industries.
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
- Manages multiple development projects, development groups and/or application support functions for a major business segment.
- Oversees the development and implementation of multiple, complex, organization-wide systems .
- Allocates and ensures resource arrangements during the application development life cycle .
- Maintains relationships with clients to ensure the implementation quality of application systems .
- Provides solutions or recommendations on complicated technology issues for application development and implementation .
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.
PNC also has fundamental expectations of our people managers. As a manager of talent in PNC, you will be expected to:
- Include Intentionally - Cultivates diverse teams and inclusive workplaces to expand thinking.
- Live the Values - Role models our values with transparency and courage.
- Enable Change - Takes action to drive change and innovation that will transform our business.
- Achieve Results - Takes personal ownership to deliver results. Empowers and trusts others in decision making.
- Develop the Best - Raises the bar with every talent decision and guides the achievement of all employees and customers.
Qualifications
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
Application Development, Business Management, Customer Solutions, Design, Generative AI, Group Problem Solving, Machine Learning, People Management, Process Improvements, Release Management, Software Engineering Management, Software Solutions, User Experience (UX) DesignCompetencies
Application Delivery Process, Application Development (Client/Server), Application Development (Object-Oriented), Application Development Tools, Application Localization and Internationalization, IT Environment, Software Process Improvement (SPI)Work Experience
Roles at this level typically require a university / college degree. Higher level education such as a Masters degree, PhD, or certifications is desirable. Industry-relevant experience is typically 8+ years. At least 5 years of prior management experience is typically required. Proven leadership experience with a moderate to large scope of responsibility is required. In lieu of a degree, a comparable combination of education, job specific certification(s), and experience (including military service) may be considered.Education
BachelorsCertifications
No Required Certification(s)Licenses
No Required License(s)Language Assessments
No Required or Preferred Language Assessments
Benefits
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
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)
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
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.
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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
PNC Financial Services Group AI Hiring
PNC Financial Services Group has 7 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, AI Safety. Positions span Pittsburgh, PA, US, Tysons Corner, VA, US. Compensation range: $143K - $228K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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