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About This Role
Forward Deployed Engineer - (Junior and Senior roles)
Responsibilities:
- Rapidly build and deploy end-to-end AI systems and core components of generative AI solutions, owning the full arc from problem framing and MVP development to deployed workflow with a bias to action
- Collaborate with technical leads, product managers, and cross-functional teams to contribute to the technical design of AI agents, orchestration patterns, and advanced context engineering modules
- Drive product advancements by developing robust RAG pipelines, context engineering workflows, and integrations with services like Azure AI Search and Databricks
- Champion production discipline by implementing rigorous testing and evaluation frameworks, including golden sets, regression harnesses, and red-teaming to ensure secure, observable, and auditable code
- Deliver in a fast-paced, outcome-accountable environment, operating in two-to-six-week cycles with direct client and stakeholder feedback to ensure solutions solve real-world problems and are ready for scale
- Deploy and maintain scalable AI microservices and containerized workloads, optimizing performance and reliability to meet enterprise standards
- Champion the use of modern development accelerators such as Claude Code, Lovable, Cursor, and others where policy permits, to increase delivery capacity and velocity while elevating code quality
- Ensure software reliability and high code quality by writing unit tests, participating in code reviews, and utilizing CI/CD pipelines for automated builds and deployments
- Engage in agile development processes including daily stand-ups, sprint planning, and retrospectives
- Act with integrity, professionalism, and personal responsibility to uphold KPMG's respectful and courteous work environment
Qualifications:
- Minimum three years of recent experience in software engineering with demonstrated success in building, testing, and shipping production AI, machine learning, or data-intensive applications
- Bachelor's degree from an accredited college or university in computer science, engineering, artificial intelligence, or a related technical field is preferred; minimum of a high school diploma or GED is required
- Certifications in areas such as Claude Certified Architect, Claude Certified Developer, OpenAI Codex Deployment Practitioner, and OpenAI Codex Professional are preferred
- Strong programming skills in Python; hands-on experience with modern AI/ML frameworks such as LangChain, LlamaIndex, or Semantic Kernel
- Practical knowledge of modern AI methodologies and engineering practices, including AI systems judgment, context engineering, agentic workflows, and evaluation discipline
- Skilled within an agile environment using SDLC tools like Azure DevOps, Git, and CI/CD pipelines for automated builds and deployments
- Solid experience with cloud platforms, preferably Azure (or GCP/AWS); familiar with containerization technologies like Docker and Kubernetes
- Familiarity with AI-first developer accelerators (such as Cursor, Claude Code, or Copilot) to drive high-velocity development
- Excellent analytical, problem-solving, and communication skills; able to communicate technical ideas clearly, thrive in ambiguous environments, and collaborate effectively with diverse stakeholders
- Ability to demonstrate prior experience with real work via a portfolio, a GitHub repository, or a structured walk-through of a system designed and shipped
KPMG LLP (“KPMG”) seeks a contractor in the United States to provide service to KPMG through one of our contracted employer/agency service providers. All applicants for any KPMG role are expected to act with integrity, professionalism, and personal responsibility to uphold the firm’s respectful and courteous work environment. All applicants must be authorized to work in the U.S. without the need for employment-based visa sponsorship now or in the future. KPMG LLP will not sponsor applicants for U.S. work visa status for this opportunity (no sponsorship is available for H-1B, L-1, TN, O-1, E-3, H-1B1, F-1, J-1, OPT, CPT or any other employment-based visa).
Nothing herein shall be deemed to create an employer-employee relationship between contractor and KPMG, nor shall contractor be considered a representative or agent of KPMG.
KPMG LLP and its subsidiaries comply with all local/state regulations in regard to displaying pay rate ranges. The pay rate range(s) displayed is/are specifically for those contracted who will perform work in or reside in the location(s) listed, if selected for the role. Pay is determined based on a variety of factors including market data, ranges, applicant's skills and prior relevant experience, certain degrees and certifications (e.g. JD, technology), and specific location, for example. Additionally, applicants may be required to apply and become employed by a service provider utilized by KPMG, and final pay rate(s) and/or eligibility for additional benefits may be determined by such provider.
KPMG LLP, its subsidiaries, and its agency service providers (including, but not limited to, MBO Partners Inc., Magnit LLC, and TalentBurst Inc.) are equal opportunity employers/contractors. All qualified applicants are considered without regard to race, color, creed, religion, age, sex/gender, national origin, ancestry, citizenship status, marital status, sexual orientation, gender identity or expression, disability, physical or mental handicap unrelated to ability, pregnancy, veteran status, unfavorable discharge from military service, genetic information, or other legally protected status.
Los Angeles County applicants: Material job duties for this position are listed above. Criminal history may have a direct, adverse, and negative relationship with some of the material job duties of this position. These include the duties and responsibilities listed above, as well as the abilities to adhere to company policies, exercise sound judgment, effectively manage stress and work safely and respectfully with others, exhibit trustworthiness, and safeguard business operations and company reputation. Pursuant to the California Fair Chance Act, Los Angeles County Fair Chance Ordinance for Employers, Fair Chance Initiative for Hiring Ordinance, and San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
Pay Rate Range
125 - 200 USD hourly
Salary Context
This $260K-$416K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At KPMG, 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($338K) sits 59% above the category median. Disclosed range: $260K to $416K.
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.
KPMG AI Hiring
KPMG has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Remote, US, New York, NY, US. Compensation range: $249K - $416K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
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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