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About This Role
Job Description
Position:
Senior Director – Internal Audit, Data Science and AI
Location:
Reston, VA / Washington, DC / Remote (USA)
Job Type:
Permanent
Salary Range:
$226,000 – $340,000 per year
Additional Compensation:
35% Long-Term Incentive Cash Bonus
Work Arrangement:
Hybrid or Remote For someone local to Washington, DC or Reston, VA, this is a hybrid role (3 or more days/week in office work requirement). If someone isnt local to the DC area, then wed consider hiring a Remote employee. If remote, they must live in the 50 US states, work Eastern Standard Time Zone Business hours (8:30-5 PM EST daily) and be willing to come into our DC or Reston, VA office at least 1 time per quarter. It could be more than that depending on business needs.
Relocation Assistance:
Available, subject to eligibility and business needs
Sponsorship:
Not available
About the Organization
Our client is a leading financial services organization focused on strengthening the housing finance ecosystem through technology, data-driven insights, and innovative risk management practices. The organization emphasizes responsible AI adoption, operational excellence, and enterprise-wide collaboration to enhance business performance, governance, and risk oversight.
Position Overview
The Senior Director – Internal Audit, Data Science and AI will lead the strategy and execution of AI, data science, analytics, and automation initiatives within the Audit function. This role will drive the development of innovative technology solutions that improve audit quality, efficiency, risk detection, continuous monitoring, and enterprise risk management.
The ideal candidate is a hands-on technical leader who can guide a highly skilled team while personally contributing to AI/ML model development, coding, and technical implementation. This position requires a balance of strategic leadership, technical expertise, product development, and collaboration with senior executives and cross-functional technology teams.
Candidates based in the Washington, DC, or Reston, VA, area will be expected to work in the office at least three days per week. Remote candidates must reside within the United States, work Eastern Time business hours (8:30 AM–5:00 PM), and be available to visit the DC or Reston office at least once per quarter, with additional visits as business needs require.
Key Responsibilities
- Lead the development and execution of the Audit functions AI, analytics, and automation roadmap, aligned with enterprise objectives and strategic priorities.
- Define and manage the strategy, product roadmap, delivery, and adoption of AI, Generative AI, advanced analytics, and automation solutions.
- Remain actively involved in hands-on coding, AI/ML model development, experimentation, and technical problem-solving while leading a small, highly technical team.
- Partner with Technology teams to design and scale unified platforms, reusable data assets, APIs, microservices, AI capabilities, and enterprise-grade solutions.
- Develop and implement AI-driven capabilities for anomaly detection, continuous monitoring, automated evidence generation, full-population testing, and control-risk reporting.
- Lead, mentor, and develop a high-performing team of data science, AI engineering, product, and automation professionals.
- Collaborate with Audit, Enterprise AI, Technology, Risk, Compliance, and business stakeholders to expand AI capabilities across the organization.
- Establish appropriate governance frameworks for data quality, model risk management, responsible AI, security, and regulatory compliance.
- Advise senior executives and VP-level stakeholders on AI strategy, emerging technologies, technical opportunities, and associated risks.
- Establish operating models, talent strategies, performance metrics, and adoption plans to drive measurable improvements in productivity, quality, and business outcomes.
- Promote reusable technology solutions and scalable delivery practices that accelerate innovation while maintaining strong controls.
Required Qualifications
- At least 8 years of relevant experience in data science, artificial intelligence, analytics, automation, product management, technology delivery, audit technology, or related enterprise technology functions.
- At least 5 years of leadership experience building, coaching, and managing high-performing technical teams.
- Proven experience delivering enterprise AI, analytics, automation, or data products from strategy through production deployment, with measurable business results.
- Experience leading AI-enabled transformation within Internal Audit, risk, compliance, financial services, or another highly regulated industry.
- Strong hands-on expertise in AI/ML, Generative AI, automation, advanced analytics, data platforms, cloud technologies, and model lifecycle management.
- Demonstrated ability to personally contribute to coding, model development, and technical implementation rather than focusing exclusively on team supervision.
- Experience designing and scaling technology platforms, reusable capabilities, APIs, microservices, governed data products, and enterprise AI solutions.
- Strong understanding of data governance, model risk, responsible AI, and regulatory requirements in financial services or similarly regulated environments.
- Excellent executive communication and stakeholder management skills, including the ability to explain complex technical concepts to non-technical audiences.
- Experience developing technical strategies, operating models, talent plans, and performance measures that drive adoption, efficiency, and business value.
- Bachelors degree or equivalent experience in Computer Science, Data Science, Statistics, Engineering, Information Systems, Business, or a related discipline.
Preferred Qualifications
- Masters degree or advanced qualification in Computer Science, Data Science, Artificial Intelligence, Engineering, Analytics, Finance, Audit, Risk, or a related field.
- Strong understanding of Internal Audit, enterprise controls, governance, or risk management.
- Experience with Generative AI, large language models (LLMs), retrieval-augmented generation (RAG), intelligent agents, natural language processing, anomaly detection, machine learning, multi-agent workflows, or knowledge graphs.
- Familiarity with cloud-based AI and data platforms, MLOps/LLMOps, model monitoring, data quality controls, secure software delivery, and enterprise architecture.
- Relevant AI, machine learning, cloud, or technology certifications, or equivalent practical experience.
Key Candidate Requirements
- Must be a hands-on technical leader who continues to write code and develop AI/ML models.
- Must be comfortable managing a small team of approximately three direct reports while remaining actively involved in technical execution.
- Must demonstrate experience taking AI solutions from concept through production adoption.
- Must possess strong leadership, communication, and cross-functional collaboration skills.
- Must be authorized to work in the United States without employer sponsorship.
- Remote candidates must reside within one of the 50 US states and be available to travel to the Reston, VA, or Washington, DC, office at least quarterly.
Compensation and Benefits
Base Salary:
$226,000 – $340,000 per year, with flexibility for an exceptional candidate.
Long-Term Incentive:
35% long-term incentive cash bonus.
Relocation Assistance:
Considered on a case-by-case basis, subject to eligibility and business needs.
Work Arrangement:
Hybrid for candidates local to Reston, VA, or Washington, DC; remote opportunities may be available elsewhere in the United States.
Important:
Candidates who focus exclusively on people management and no longer perform hands-on coding or AI/ML model development may not be suitable for this position.
Salary Context
This $226K-$340K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At I8IS INC., 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 $211,950 based on 13,048 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,300. This role's midpoint ($283K) sits 34% above the category median. Disclosed range: $226K to $340K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
I8IS INC. AI Hiring
I8IS INC. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Reston, VA, US. Compensation range: $340K - $340K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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