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About This Role
BBB National Programs
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BBB National Programs is where businesses turn to enhance consumer trust and consumers are heard.
Director, AI \& Intelligent Systems
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*\*\*This role requires a minimum of three in\-office days per week in McLean, VA to perform the essential functions of the position.*
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WHO WE ARE
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BBB National Programs, a non\-profit organization, is the home of U.S. independent industry self\-regulation, currently operating more than 20 globally recognized programs that have been helping enhance consumer trust in business for more than 50 years. These programs provide third\-party accountability and dispute resolution services that address existing and emerging industry issues, create fair competition for businesses, and a better experience for consumers. BBB National Programs continues to evolve its work and grow its impact by providing business guidance and fostering best practices in advertising, child\-and\-teen\-directed marketing, data privacy, dispute resolution, automobile warranty, technology, and emerging areas.
POSITION SUMMARY
The Director, AI \& Intelligent Systems serves as BBB National Programs' subject matter expert on artificial intelligence designing, building, orchestrating and executing AI capabilities and integration across the organization's product portfolio and partner ecosystem. The Director role is hands\-on, personally designing, building, and rolling out solutions to execute the AI strategy, rather than directing a team to deliver them. This role serves as the central integration point for AI across the organization's technology ecosystem, connecting capabilities, platforms, and vendors into a coherent whole rather than owning any single piece in isolation. The Director is responsible for weaving AI into and across existing systems including the Decisions low\-code platform, the AWS AI/ML estate, and any future tools so that AI adoption happens as an integrated capability. The Director will translate business needs across our Programs and Corporate Functions into AI\-powered solutions leveraging vendor relationships, cloud\-native services, and emerging AI frameworks to deliver measurable outcomes aligned with BBB National Programs' mission.
YOUR IMPACT
*Essential Duties and Responsibilities*
AI Strategy \& Roadmap
- Execute the multi\-year AI roadmap across BBB National Programs' ecosystem
- Identify, prioritize, and justify AI use cases in collaboration with leadership
- Present AI strategy, progress, and ROI to the CIO
- Stay current on AI/ML advancements
AI Orchestration \& Integration
- Design and deploy agentic workflows, LLM integrations, and intelligent automation pipelines using AWS Bedrock, Step Functions, Lambda, and connected APIs
- Integrate AI capabilities with the Decisions low\-code platform and existing systems via API orchestration
- Build and manage retrieval\-augmented generation (RAG) pipelines, vector databases, and prompt engineering frameworks
- Evaluate and select AI tools, models, and platforms appropriate to each use case (build vs. buy vs. configure)
- Connect Power BI to the AI/ML estate to enable natural\-language querying, automated insight generation, and predictive visuals — evolving reporting from static dashboards into an AI\-connected decision layer
Vendor \& Outsourced Development Governance
- Serve as the primary relationship owner and technical point of contact with the Decisions platform vendor
- Define technical requirements, acceptance criteria, and delivery standards for all outsourced Decisions development work
- Manage vendor SLAs, sprint reviews, and escalation processes to ensure quality and alignment with BBB objectives
- Maintain institutional knowledge of all Decisions\-based workflows, configurations, and integrations
AWS AI/ML Estate Ownership
- Ensure cost\-effective, secure, and scalable use of cloud AI resources in alignment with AWS best practices
- Collaborate with IT and security teams on access controls, data governance, and compliance within the AWS environment
Internal Enablement \& Change Management
- Build AI literacy across business units through training, workshops, and accessible documentation
- Partner with leaders to embed AI tools into day\-to\-day workflows
- Champion adoption of AI\-powered capabilities and manage organizational change effectively
WHAT YOU WILL BRING
*Must have:*
- 7\+ years of progressive experience in technology roles, with at least 2\+ years focused on AI/ML implementation and deployment
- Demonstrated experience owning a technology or AI strategy/roadmap, including prioritizing initiatives, securing stakeholder buy\-in, and reporting progress and ROI to senior leadership or executives
- Demonstrated experience designing and deploying AI or intelligent automation solutions in an enterprise or organizational setting
- Experience with LLM APIs, prompt engineering, and agentic AI frameworks (e.g., LangChain, AutoGen, or equivalent)
- Experience integrating AI capabilities via APIs and managing data pipelines, vector databases, or RAG architecture
- Proven ability to manage vendor relationships, write technical requirements, and evaluate third\-party deliverables
- Strong communication skills with the ability to translate technical concepts for non\-technical executives and stakeholders
*Let us know if you have:*
- Fortune 500 experience
- Experience with low\-code/no\-code platforms (Decisions, Appian, OutSystems, or equivalent) in a governance or oversight capacity
- Familiarity with AI governance frameworks, responsible AI principles, and emerging AI regulations
- Experience building or managing AI products in consumer\-facing or trust\-sensitive contexts
- AWS experience (e.g., AWS Certified Machine Learning – Specialty, AWS Solutions Architect)
WHAT WE OFFER:
At BBB National Programs, we bring a growth mindset as we advance our mission and strive to foster trust, innovation, and competition in the marketplace, while cultivating a team of talented and engaged professionals who seek out new challenges and opportunities to catalyze our progress. We are an inclusive organization, bringing a dynamic environment that supports our employees and the meaningful work we do.
*Health \& Welfare Benefits*: You will be offered a comprehensive plan offering health, dental and vision plans, paid short\-term disability insurance, and life insurance.
*Financial Well\-Being*: Build your retirement savings with our 401k plan matching up to 7% of your contributions.
*Time Off*: You will have flexibility for the time you need off from work. We offer a variety of plans including vacation, personal, and wellness leave.
*Wellness*: We promote physical and mental wellness by providing a fully equipped on\-site fitness center in our offices and an employee assistance program.
*Environment*: Our modern headquarters in McLean, VA, and our office in New York, NY provide the space for creativity and collaboration, and the technology resources so you can be at your best. We believe that an inclusive workforce is a strength in fulfilling our mission.
*The anticipated hiring range for this position is $130,000 – $150,000 annually. Actual compensation will be based on relevant experience, skills, education, and other job\-related factors.*
BBB National Programs is Great Place to Work® Certified and has been named as a Best Place to Work for Working Daughters.
BBB National Programs is proud to be an equal employment opportunity employer.
Salary Context
This $130K-$150K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At BBB National Programs, 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($140K) sits 36% below the category median. Disclosed range: $130K to $150K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
BBB National Programs AI Hiring
BBB National Programs has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in McLean, VA, US. Compensation range: $150K - $150K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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