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About This Role
NKSFB, LLC is the largest business management firm in the country, with more than 650 employees working from seven offices in the Los Angeles and New York City area. We work alongside the world’s top artists in music, film, and television, as well as athletes, executives, entrepreneurs, and other high achievers, offering a sophisticated range of concierge\-style services to meet their lifestyle management needs.
Your Role:
The Applied AI \& Automation Engineer is part of the IT Enterprise Applications team reporting to the Director – Enterprise Applications, focused on designing, building, and deploying AI\-driven solutions and intelligent automations that transform workflows across the firm's Business Management, Tax, Audit, and shared services departments. The role identifies automation opportunities, develops intelligent workflows, and implements AI\-powered tools while partnering with department leaders, the Helpdesk, IT Services, business teams, vendors, and other IT groups.
Success in this position requires strong technical skills, sound judgment, clear communication, and the ability to manage priorities in a fast\-paced professional services environment. The ideal candidate is a hands\-on builder, proactive innovator, and skilled in applied AI development, robotic process automation, process optimization, and translating complex business needs into reliable, scalable automated solutions.
What You Will Do:
Design, develop, and deploy intelligent automations using various AI and RPA tools across tax, audit, business management, document management, workflow, and administrative functions.
Build and maintain RPA workflows for repetitive, high\-volume tasks such as data entry, report generation, document processing, invoice handling, and client onboarding/offboarding.
Develop AI\-powered agents and copilots using Copilot Studio and Azure AI Foundry to enhance staff productivity and create intelligent assistants tailored to firm\-specific use cases.
Create and manage Power Automate Desktop flows for attended and unattended automation scenarios, including legacy application integrations that lack APIs.
Partner with department leaders and end users across Business Management, Tax, and Audit to identify manual workflows and processes that are prime candidates for AI\-driven automation.
- Conduct process discovery sessions to map existing workflows, quantify time savings, and prioritize automation initiatives based on business impact and feasibility.
- Measure and report on automation ROI — tracking key metrics such as hours saved, error reduction, throughput improvement, and user adoption.
- Evaluate and pilot emerging AI and automation tools proactively as the technology landscape and industry evolve, recommending new platforms and approaches to IT leadership.
- Monitor vendor AI capabilities in firm\-standard platforms and assess opportunities to leverage built\-in AI features.
- Develop API integrations connecting AI and automation solutions with enterprise applications, data sources, and third\-party platforms.
- Act as an internal AI champion — hosting workshops, creating playbooks, and training team members and business users on the safe and effective use of AI and automation tools.
- Create and maintain comprehensive documentation for all automations, including process diagrams, error handling procedures, and user guides.
- Follow security policies when implementing AI and automation, particularly when handling confidential client data, PII, and financial records.
- Support SOC\-2 compliance and audit readiness by documenting automated processes and maintaining separation of duties where applicable.
- Collaborate on innovation initiativeswith IT leadership to align AI and automation projects with the firm's strategic goals.
- Stay current on emerging methods to improve accuracy, efficiency, and user experience.
What Do You Need to Succeed:
- Communicate professionally with users, IT teams, vendors, and stakeholders.
- Escalate issues appropriately when additional support or vendor involvement is required.
- Provide updates to leadership on project status, risks, priorities, and improvement opportunities.
- Maintain confidentiality when handling access, firm data, and client information.
- Support a team\-oriented environment with responsiveness and professionalism.
- Perform related duties as assigned.
- Bachelor's degree in Computer Science, Information Systems, Data Science, or a related field, or an equivalent combination of education and relevant work experience. 5 to 10 years of combined experience in software development, application support, automation engineering, or related technical roles preferred.
- Hands\-on experience building solutions with Microsoft Power Platform (Power Automate, Power Automate Desktop, Power Apps, Copilot Studio).
- Experience with UiPath or similar enterprise RPA platforms.
- Familiarity with Azure AI services (Azure AI Foundry, Azure OpenAI Service, Azure Cognitive Services, AI Builder).
- Experience with Microsoft 365 Copilot extensibility and customization.
- Working knowledge of Python, SQL, or other scripting/programming languages for data transformation, API integration, and automation scripting.
- Exposure to or interest in agentic AI frameworks, LLM orchestration, and prompt engineering.
- Experience working in a Windows environment.
- Coursework or experience in accounting and/or bookkeeping.
- Familiarity with business management, audit, and tax processes and applications.
What Will Set You Apart:
- Strong organizational and time management skills.
- High attention to detail and accuracy.
- Sound judgment and discretion.
- Excellent interpersonal and communication skills.
- Strong analytical and problem\-solving abilities.
- Ability to work collaboratively and effectively with others.
- Ability to manage and prioritize multiple tasks.
- Proactive mindset with an interest in identifying opportunities and driving innovation, not just executing tasks.
- Capacity to manage multiple projects with competing deadlines and priorities.
- Willingness to learn new tools and technologies rapidly as the AI and automation landscape evolves.
- Commitment to maintaining confidentiality.
Salary Range: The salary range for this role is $125,000 \- $175,000 and represents the firm's good faith and reasonable estimate of the range of possible compensation at the time of posting. Actual compensation will be dependent upon several factors, including but not limited to, the candidate's relevant experience, qualifications, and location.
What We Offer: NKSFB is proud to provide a generous range of benefits and rewards designed to support the well\-being, security, and overall satisfaction of our employees. As part of our team, you can look forward to:
- Accrue 15 PTO days annually, giving you time to rest and recharge.
- A 401(k) plan with quarterly employer match contributions, when eligible, to help you build your financial future.
- Ten paid holidays each year, plus one extra floating holiday for added flexibility.
- Summer work hours, to allow you to leave work early on designated dates.
- Hybrid work schedules, if applicable, to increase commute convenience.
- Comprehensive medical, dental, and vision coverage to support your health and wellness.
- Access to Flexible Spending Accounts to help manage eligible healthcare and dependent care expenses.
- Basic Life and AD\&D insurance for added peace of mind.
- Voluntary Life and AD\&D coverage for additional protection.
- Long\-term Disability benefits to support you during unexpected challenges.
- Voluntary Critical Illness, Cancer, Hospital Indemnity, and Accident coverage for expanded financial security.
- Wellness activities to promote awareness of the importance of your health across all aspects of your life.
Pet insurance options so your furry family members are covered too.
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*The job duties listed are typical examples of work performed by positions in this job classification and are not designed to contain or be interpreted as a comprehensive inventory of all duties, tasks and responsibilities. Employees may also perform other duties as assigned.*
*NKSFB, LLC* *is an Equal Opportunity Employer and is committed to diversity and inclusion. If you’re a qualified candidate with a disability and you need reasonable accommodation to apply for this position, please contact us at [email protected].*
W*e collect your personal information when you apply for employment at NKSFB, LLC. To learn more about our data privacy practices, please view our* *Employee Privacy Policy.*
Salary Context
This $125K-$175K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At NKSFB, LLC, 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 $181,170 based on 12,692 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($150K) sits 17% below the category median. Disclosed range: $125K to $175K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
NKSFB, LLC AI Hiring
NKSFB, LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Los Angeles, CA, US. Compensation range: $175K - $175K.
Location Context
AI roles in Los Angeles pay a median of $191,580 across 1,792 tracked positions. That's 4% below 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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