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About This Role
The Opportunity
As a Director, Solution Architect, AI\-Native Engineering, you will lead the charge in developing innovative technology solutions within our Internal Firm Services practice. You will focus on implementing and delivering cutting\-edge software solutions to clients, enabling seamless integration and efficient project execution. Your role will involve managing the end\-to\-end delivery process and collaborating with cross\-functional teams to drive successful technology implementations.
As a Director, you will set the strategic direction and inspire others to follow. Your role is crucial in driving business growth, shaping the direction of client engagements, and mentoring the next generation of leaders. You are expected to be a guardian of PwC's reputation, understanding that quality, integrity, inclusion, and a commercial mindset are all foundational to our success. You will create a healthy working environment while maximizing client satisfaction and actively teaming across the PwC Network.
In this role at PwC, you will develop new ideas, solutions, and structures, driving thought leadership and promoting technological advances. You will identify gaps in the market and convert opportunities to success for the firm, adhering to professional and technical standards.
Responsibilities
- Leading the development and implementation of innovative software solutions to meet client needs
- Managing end\-to\-end technology delivery processes, confirming seamless integration and efficient execution
- Collaborating with cross\-functional teams to drive successful technology implementations and project outcomes
- Overseeing the creation and maintenance of product roadmaps and technology strategies
- Guiding teams in developing user stories and deliverable planning to enhance project delivery
- Promoting technological advances and fostering an environment where people and technology thrive together
- Identifying market gaps and converting opportunities into successful solutions for the firm
- Mentoring and developing the next generation of leaders within the organization
- Balancing strategic choices with system\-level enablers to drive change and innovation
- Upholding professional and technical standards, adhering to the firm's code of conduct and independence requirements
What You Must Have
- Bachelor's degree or, in lieu of a degree, demonstrating in addition to the minimum years of experience required for the role, three years of Technology Delivery work experience for each missing year of college is required
- At least 8 years of experience with at least 5 years of AI technology experience (RAGs, LLM, AI Adoption)
What Sets You Apart
- Excelling in IT Business Strategy and IT Consulting
- Demonstrating proficiency in Software Development and Software Product Management
- Leading software development and project management initiatives in Claude Code, Cursor, GitHub, Copilot, etc
- Managing IT Infrastructure and IT Service Management (ITSM)
- Developing User Stories and embracing Rapid Experimentation
- Delivering Project Management and Project Risk solutions
- Promoting Process Improvement and Quality Assurance Process Management
The salary range for this position is: $122,500 \- $423,780\. For residents of Washington state the salary range for this position is: $122,500 \- $504,500\. Actual compensation within the range will be dependent upon the individual's skills, experience, qualifications and location, and applicable employment laws. All hired individuals are eligible for an annual discretionary bonus. PwC offers a wide range of benefits, including medical, dental, vision, 401k, holiday pay, vacation, personal and family sick leave, and more. To view our benefits at a glance, please visit the following link: https://pwc.to/benefits\-at\-a\-glance
As PwC is an equal opportunity employer, all qualified applicants will receive consideration for employment at PwC without regard to race; color; religion; national origin; sex (including pregnancy, sexual orientation, and gender identity); age; disability; genetic information (including family medical history); veteran, marital, or citizenship status; or, any other status protected by law.
PwC does not intend to hire experienced or entry level job seekers who will need, now or in the future, PwC sponsorship through the H\-1B lottery, except as set forth within the following policy: https://pwc.to/H\-1B\-Lottery\-Policy.
Learn more about how we work: https://pwc.to/how\-we\-work
For only those qualified applicants that are impacted by the Los Angeles County Fair Chance Ordinance for Employers, the Los Angeles' Fair Chance Initiative for Hiring Ordinance, the San Francisco Fair Chance Ordinance, San Diego County Fair Chance Ordinance, and the California Fair Chance Act, where applicable, arrest or conviction records will be considered for Employment in accordance with these laws. At PwC, we recognize that conviction records may have a direct, adverse, and negative relationship to responsibilities such as accessing sensitive company or customer information, handling proprietary assets, or collaborating closely with team members. We evaluate these factors thoughtfully to establish a secure and trusted workplace for all.
Salary Context
This $122K-$504K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1956 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 3,739 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At PwC, 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 $179,000 based on 11,901 positions with disclosed compensation. Director-level AI roles across all categories have a median of $243,000. This role's midpoint ($313K) sits 75% above the category median. Disclosed range: $122K to $504K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
PwC AI Hiring
PwC has 11 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, AI Engineering Manager. Positions span New York, NY, US, Stamford, CT, US, Los Angeles, CA, US. Compensation range: $100K - $504K.
Location Context
AI roles in New York pay a median of $210,000 across 2,448 tracked positions. That's 5% above the national 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 3,739 open positions tracked in our dataset. By seniority: 115 entry-level, 1,764 mid-level, 1,444 senior, and 416 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (597 positions). The remaining 3,119 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,739 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,650), Data Scientist (271), AI Software Engineer (252). 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 (115) are outnumbered by mid-level (1,764) and senior (1,444) 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 416 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (597 positions), with 3,119 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 $200,000. Top-quartile roles start at $253,000, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,929 postings), Aws (1,185 postings), Azure (869 postings), Rag (866 postings), Gcp (726 postings), Prompt Engineering (578 postings), Pytorch (575 postings), Claude (547 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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