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About This Role
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 Solution Architect Expert within PNC's Corporate & Institutional Banking organization (C&IB) you will be based in Pittsburgh, PA or Dallas, TX.
Lead AI Solution Architect, Commercial Lending Modernization
We are rebuilding our commercial lending technology with agentic AI, starting with a full rewrite of the legacy commercial real estate loan servicing platform.
The Lead AI Solution Architect is the senior technical lead embedded in a small, forward-deployed team of AI engineers and AI agents doing that work. The person will own the target architecture for the new platform, make the key design calls in real time, and stay hands-on through delivery. They won’t hand designs off to someone else.
This is the first of several AI-led modernization pilots, and it sets the pattern for the rest. After servicing, the program moves into origination, underwriting, compliance, funding, booking and accounting, about 2-3 years in total. This person shapes what modern commercial lending looks like at PNC.
Required Qualifications
- Multiple years of experience in software engineering and solution architecture, including technical lead on at least one large legacy modernization (refactor, re-platform or rewrite of a core system)
- Hands-on experience building production systems with LLMs and agentic AI: agent orchestration, tool use, RAG, evaluations, guardrails and human-in-the-loop design
- Experience using AI-assisted engineering to analyze, document and rewrite legacy code at scale, including reverse-engineering business rules out of older codebases
- Strong cloud-native architecture background: microservices, APIs, event-driven design, containers/Kubernetes, AWS or Azure
- Still writes code (Python, Java or similar) and is comfortable pairing with engineers on implementation
- Clear communicator who can explain tradeoffs to engineers, business partners and senior leaders
Preferred Qualifications
- Commercial lending or commercial real estate domain knowledge, especially loan servicing, payments, escrow or investor reporting
- Financial services or other regulated-industry experience: security, resiliency, auditability, model risk and controls built into the design
- Forward-deployed engineer, AI solutions consultant or startup technical founder background
- Experience with agent frameworks and protocols (LangGraph, Semantic Kernel, AutoGen, MCP or similar) and AI observability tools
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
- Uses advanced technical knowledge and industry experience to develop and drive solution architecture design strategy. Utilizes strong understanding of the broader picture of systems, programs, and initiatives to actualize solutions to interconnect systems.
- Leads technical design sessions with the development teams, including the creation of class models, sequence diagrams, component models, and detailed design specifications to ensure that architecture standards are followed.
- Provides architectural leadership on a variety of issues in software designs, system architecture, and solution-oriented technologies. Serves as a knowledge resource and thought leader to advise on critical issue management.
- Communicates implications of architectural decisions, technology costs/benefits, and implementation requirements. Maintains strong executive presence to communicate and guide strategy to peers and executive-level LOB and Technology leaders.
- Demonstrates expertise operating as a success engineer to manage solution architecture project outcomes, including hardware and software design; ensuring composite architecture is scalable, reliable, available, and manageable.
- Standardizes and implements scalable and reliable engineering and deployment patterns for common technologies. Ensures solution designs meet PNC’s needs for confidentiality, integrity, and availability and adhere to the our policies, standards, and procedures.
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
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
Cloud Computing, Design, Enterprise Architecture (EA), Generative AI, Requirements Analysis, Security Architecture Design, Solutions Development, System Integration, Technical Architecture, Technical Leadership, Technology StrategiesCompetencies
Application Design, Architecture, Crisis Management, Effectiveness Measurement, Industry Knowledge, IT Standards, Procedures & Policies, Platform Architecture, Target Architecture, The Open Group Architecture Framework (TOGAF), Transitional ArchitectureWork Experience
Roles at this level typically require a university / college degree. Higher level education such as a Masters degree, or PhD is desirable. Industry experience is typically 8 + years. 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
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 9,079 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 Required
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 $211,950 based on 13,048 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 $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
PNC Financial Services Group AI Hiring
PNC Financial Services Group has 3 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Farmers Branch, TX, US, Vienna, VA, US, Pittsburgh, PA, US. Compensation range: $143K - $202K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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