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About This Role
Why We Are \- Who We Are: About FFF Enterprises
What We Do \- https://www.fffenterprises.com/company/what\-we\-do.html
Position Summary
The Director of AI/ML leads the strategy, architecture, and delivery of enterprise artificial intelligence and robotic process automation (RPA) capabilities across the organization’s data platform. This role is responsible for building and leading a team of AI engineers and automation developers delivering machine learning, generative AI, multi\-agent systems, and intelligent automation solutions. The Director partners closely with Data Engineering, Reporting, and business teams to operationalize AI and automation capabilities using Databricks and Microsoft technologies. This role ensures solutions are scalable, secure, and governed while delivering measurable operational efficiency and business value.
Essential Functions and Duties
AI Strategy \& Architecture Leadership
Lead the development and execution of enterprise AI and intelligent automation strategy.
Activities include:
- Define and implement the enterprise AI architecture using Databricks AI capabilities.
- Establish architectural standards for generative AI, machine learning models, and agent\-based systems.
- Design frameworks for multi\-agent AI systems that support business workflows and decision\-making.
- Identify high\-impact AI and automation opportunities across business units.
- Align AI initiatives with the enterprise data platform and overall technology strategy.
AI Engineering \& Automation Leadership
Build and lead a high\-performing team responsible for delivering AI and intelligent automation solutions.
Activities include:
- Lead, mentor, and develop a team of AI engineers, machine learning engineers, and automation developers.
- Establish engineering standards and best practices for AI development and automation implementation.
- Oversee the design, development, testing, and deployment of AI and automation solutions.
- Coordinate work across AI engineering, data engineering, reporting teams, and business stakeholders.
Agentic AI \& Generative AI Platforms
Lead the design and implementation of modern AI architectures including generative AI and agentic systems.
Activities include:
- Implement multi\-agent AI architectures for automation and operational decision support.
- Deploy AI solutions leveraging Databricks capabilities including MLflow, vector search, and model serving.
- Implement Retrieval Augmented Generation (RAG) architectures using governed enterprise data.
- Lead adoption of conversational AI platforms including Databricks Genie and Microsoft Copilot integrations.
Intelligent Automation \& RPA Leadership
Lead the robotic process automation (RPA) function within the department and drive enterprise automation initiatives.
Activities include:
- Define the enterprise strategy for robotic process automation using Microsoft Power Automate.
- Identify business processes suitable for automation and prioritize initiatives based on operational impact.
- Oversee the design and development of automation workflows and automated business processes.
- Integrate AI models and agent\-based systems into automation workflows to enable intelligent decision\-making.
- Establish governance, monitoring, and reliability standards for automation solutions.
- Collaborate with business units to streamline processes and reduce manual effort through automation.
AI Governance \& Responsible AI
Ensure AI and automation solutions comply with enterprise governance, security, and compliance requirements.
Activities include:
- Establish policies and standards for responsible AI and intelligent automation.
- Ensure compliance with enterprise security, privacy, and data governance policies.
- Implement evaluation frameworks to measure AI performance, accuracy, and reliability.
- Maintain transparency and auditability of automated and AI\-driven decisions.
Cross\-Functional Collaboration
Partner with technology and business leaders to operationalize AI and automation capabilities.
Activities include:
- Collaborate with Data Engineering teams to leverage curated enterprise data for AI solutions.
- Partner with Reporting and Analytics teams to embed AI outputs into business insights and decision tools.
- Work with business stakeholders to translate operational challenges into AI\-enabled and automated solutions.
- Communicate AI and automation capabilities, risks, and value to both technical and non\-technical audiences.
Organizational \& Professional Responsibilities
- Adheres specifically to all company policies and procedures, Federal and State regulations, and laws.
- Displays dedication to position responsibilities and achieves assigned goals and objectives.
- Represents the Company in a professional manner and appearance.
- Works effectively with co\-workers, internal and external customers by sharing ideas constructively and addressing issues collaboratively.
- Keeps stakeholders informed of work progress, timelines, and issues.
- Complies with the policies and procedures stated in the Injury and Illness Prevention Program.
- Ensures conduct is consistent with all Compliance Program Policies when engaging in company activities.
- Performs other duties as assigned.
Education, Knowledge, Skills, and Experience
Required Education:
Bachelor’s Degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related field, or four (4\) years relevant experience in lieu of degree.
Required Knowledge:
- Advanced knowledge of artificial intelligence, machine learning, and generative AI technologies.
- Knowledge of agent\-based AI architectures and multi\-agent systems.
- Knowledge of robotic process automation (RPA) concepts and intelligent automation strategies.
- Understanding of enterprise AI governance, security, and model lifecycle management.
Preferred Knowledge:
- Familiarity with Microsoft Copilot platforms and conversational AI technologies.
- Familiarity with Databricks AI platform capabilities including MLflow and vector search.
- Knowledge of vector databases, embeddings, and semantic AI architectures.
Required Experience:
- At least 10 years of experience in artificial intelligence, machine learning, automation, or related technical roles, including 5 years of leadership experience managing technical teams
Preferred Experience:
- At least 12–15 years of progressive experience in AI engineering, machine learning engineering, intelligent automation, or enterprise data platform development.
- Experience leading AI and automation initiatives using modern platforms such as Databricks, Microsoft Copilot, and Microsoft Power Automate is strongly preferred.
Required Skills:
- Strong leadership and team management capabilities.
- Expertise designing enterprise AI architectures and machine learning platforms.
- Experience implementing generative AI and agent\-based systems.
- Experience with Databricks AI capabilities including MLflow and model serving.
- Experience integrating AI capabilities with Microsoft Copilot platforms.
- Strong strategic thinking and problem\-solving abilities.
- Excellent verbal and written communication skills.
- Ability to translate business challenges into AI and automation solutions.
Preferred Skills:
- Experience implementing robotic process automation using Microsoft Power Automate.
- Experience with conversational AI platforms such as Databricks Genie.
- Experience implementing multi\-agent orchestration frameworks.
- Experience integrating AI and automation systems with enterprise applications such as ERP, CRM, or logistics platforms.
- Experience with AI\-based video or image processing technologies.
Physical requirements
Vision, hearing, speech, movements requiring the use of wrists, hands and/or fingers. Must have the ability to view a computer screen for prolonged periods and the ability to sit for extended periods. Must have the ability to work the hours and days required to complete the essential functions of the position, as scheduled. The employee occasionally lifts to 20 lbs. and occasionally kneels and bends. Must have the ability to travel occasionally. Working condition include normal office setting.
Mental Demands
Learning, thinking, concentration and the ability to work under pressure, particularly during busy times. Must be able to pay close attention to detail and be able to work as a member of a team to ensure excellent customer service. Must have the ability to interact effectively with co\-workers and customers, and exercise self\-control and diplomacy in customer and employee relations’ situations. Must have the ability to exercise discretion as well as appropriate judgments when necessary. Must be proactive in finding solutions.
Direct Reports
Yes or No
EEO/AAP Statement
FFF Enterprises and Nufactor are an equal opportunity employer and prohibits discrimination and harassment based on the following characteristics: race, color, religion, national origin, physical or mental disability, gender, age (40 years and over) qualified veteran and any other characteristic protected by state or federal anti\-discrimination law covering employment. These categories are defined according to Government Code section 12920\. The Company prohibits unlawful discrimination based on the perception that anyone has any of those characteristics or is associated with a person who has or is perceived as having any of those characteristics.
Acknowledgement
The above statements are intended to describe the general nature and level of work being performed by the incumbent assigned to this classification. They are not intended to be construed as an exhaustive list of all responsibilities, duties, and/or skills required of all personnel so classified.
The undersigned employee acknowledges receipt of the Job Description for the employee’s position and understands the essential functions, responsibilities, and qualifications of the position. Furthermore, the employee acknowledges that this Job Description does not include all the essential functions of this position, and that these essential functions may change as deemed necessary by the manager.
To be considered for a position with FFF Enterprises, Inc, applicants must complete and sign the application.
Employee Benefits Available for FFF Enterprises Team Members
Employee benefits include:
- Medical Insurance
- Dental Insurance
- Vision Discount Program
- Vision Insurance Plan
- Health Savings Account (HSA)
- Flexible Spending Account (FSA)
- Dependent Care Flexible Spending Account
- Employee Assistance Program (EAP)
- Group Life and AD\&D
- Voluntary Supplemental Life Insurance Plans
- Short Term Disability
- Long Term Disability Income Protection
- 401k Profit Sharing Retirement Plan \- Discretionary Match
- Discretionary Bonus
- Supplemental Insurance Plans
- Prepaid Legal/Identity Theft Plan
- Paid Holidays/Vacation/Sick Days
- Seven (7\) Paid Holidays, Two (2\) Week Vacation, Five (5\) Sick Days, and One (1\) Float Day for CA, NC, and TX
All Other States Receive One Hundred Twenty (120\) Hours of PTO
- Tuition Reimbursement Program
- Notary Services
- Employee Referral Bonus
- Vendor Discount Programs
- Corporate Individual Travel Program
(Note: We comply with the ADA and consider reasonable accommodation measures that may be necessary for eligible applicants/employees to perform essential functions. Hire may be subject to passing a medical examination, and to skill and agility tests.)
Salary Context
This $220K-$240K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At FFF Enterprises, 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 $166,983 based on 13,781 positions with disclosed compensation. Director-level AI roles across all categories have a median of $244,288. This role's midpoint ($230K) sits 38% above the category median. Disclosed range: $220K to $240K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
FFF Enterprises AI Hiring
FFF Enterprises has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Flower Mound, TX, US, Temecula, CA, US. Compensation range: $51K - $240K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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