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About This Role
Opportunity ID
9560
Department
Practice Management
Location(s)
Austin
State
Texas
Function
GenAI
Job Description
As CohnReznick grows, so do our career opportunities. As one of the nation’s top professional services firms, CohnReznick creates rewarding careers in advisory, assurance, and tax with team members who value innovation and collaboration in everything they do!
CohnReznick helps organizations optimize performance, manage risk, and maximize value through CohnReznick LLP (assurance services) and CohnReznick Advisory LLC (advisory and tax services). Together, the firm provides leaders with deep industry knowledge and relationships, solutions to address clients’ unique business goals and risks, and insight on how emerging market forces can drive opportunity. With offices nationwide, the firm serves organizations around the world as an independent member of Nexia.
We currently have an exciting career opportunity for an Data Privacy and AI Governance Managerto join our Legal team.
CohnReznick is a hybrid firm and most of our professionals are located within a commutable distance to one of our offices. This position is considered remote which means it does not require job duties be performed within proximity of a CohnReznick office location. However, as a remote employee, you may be required to be present at a CohnReznick office with scheduled notice for client work, team meetings, or trainings
WHY COHNREZNICK?
At CohnReznick, we’re united by a common mission to create opportunity, value, and trust for our clients, our people, and our communities. Whether it’s working alongside your peers to solve a client challenge, or volunteering together at the local food bank, there are so many ways to find your “why” at the firm.
We believe it’s important to balance work with everyday life – and make time for enjoyment and fun. We invest in a robust Total Rewards package that includes everything from generous PTO, a flexible work environment, expanded parental leave, extensive learning \& development, and even paid time off for employees to volunteer.
YOUR ROLE.
Responsibilities include but not limited to:
- Develop, implement, and maintain the organization’s data privacy program including an AI governance framework including policies, procedures, and controls
- Ensure compliance with applicable data privacy and AI laws and regulations.
- Partners with business, legal, and technology teams to manage privacy risks, support data governance initiatives, and embed privacy\-by\-design principles across the organization.
- Partner closely with Technology, AI and Governance, Risk and Compliance teams to ensure internal controls are in place and effective for AI usage and personal data processing, align enterprise data standards with regulatory and compliance requirements, and support policy development and guidelines on data handling and responsible AI use.
- Establish continuous discovery mechanisms to identify, map, assess, and mitigate “Shadow AI” tools and use cases deployed without authorization.
- Integrate AI governance and privacy requirements into product development, procurement, and implementation and ensure data and information governance aligns, enables and supports the adoption of AI tools and technologies.
- Oversee governance for agentic AI and autonomous workflows, including human\-in\-the\-loop protocols and manual override requirements.
- Lead, facilitate and work with AI Governance Committee to define governance process, risk tiers and approval pathways based on materiality, impact, and regulatory exposure.
- Assist in developing and delivering training programs and communications to promote a culture of responsible AI use across the organization.
- Report on program status, risk metrics, and compliance issues to senior leadership and relevant stakeholders.
- Stay up\-to\-date on emerging AI regulations (e.g., AI Act) and data privacy laws.
YOUR EXPERIENCE.
The successful candidate will have:
- Education \& Experience: Bachelor’s degree in a relevant field (e.g., Computer Science, Data Science, Law, Information Systems, or similar). 5\+ years of experience in data privacy, compliance, or risk management roles, or equivalent experience implementing technology governance or privacy programs including experience in leading AI governance.
- Regulatory Knowledge: Strong knowledge of data protection regulations (such as GDPR, CCPA, etc.) and familiarity with newer AI governance frameworks such as the EU AI Act risk tiers, NIST AI RMF, and ISO/IEC 42001\.
- AI/ML Familiarity: Understanding of artificial intelligence and machine learning concepts and their risks, including data provenance, explainability limits, model drift, algorithmic bias audits, and guardrails against prompt injection and training data leakage (deep technical expertise is not required, but must be comfortable collaborating with technical teams).
- AI Evaluations Familiarity: Familiarity with AI evaluation frameworks and methodologies, and experience designing or conducting AI evaluations.
- Policy \& Program Management: Proven ability to develop and enforce policies or governance frameworks. Experience conducting risk assessments or audits regarding technology or data initiatives. Strong project management skills to drive governance programs.
- Collaboration \& Communication: Excellent communication and interpersonal skills. Ability to work cross\-functionally and influence stakeholders at all levels. Capable of translating complex technical or regulatory information into clear, actionable guidance for business teams.
- Leadership \& Ethics: Demonstrated commitment to ethical technology use and protecting privacy. Experience leading or coordinating cross\-departmental initiatives or committees. Strong analytical decision\-making and problem\-solving capabilities.
Preferred Qualifications (Nice to Have):
- Advanced degree in Law, Business, Data Science, or related field, or certifications in privacy or risk management (e.g., CIPM, CIPT, CIPP, or similar).
- Experience implementing AI governance frameworks or privacy compliance programs within a complex organization, especially in regulated industries (finance, healthcare, etc.).
- Experience using AI GRC automation tools such as OneTrust or Credo AI.
- Experience engaging with regulators, auditors, or industry consortia on compliance or AI governance topics.
This Data Privacy and AI Governance Manager role offers the opportunity to shape how our company innovates responsibly. If you are passionate about trustworthy AI and data protection and have the expertise to bridge technology with compliance, we encourage you to apply.
In addition, please take a moment to review ourUniversal Job Standards.
Studies have shown that we are less likely to apply to jobs unless we meet every single qualification. At CohnReznick, we are dedicated to building a diverse, equitable, and inclusive workplace, so if you’re excited about this role but your experience doesn’t align perfectly with every qualification in the job description, we still encourage you to apply. You may be just the right candidate for this or one of our other roles.
"CohnReznick" is the brand name under which CohnReznick LLP and CohnReznick Advisory LLC and their respective subsidiaries provide professional services. CohnReznick LLP and CohnReznick Advisory LLC (and their respective subsidiaries) practice in an alternative practice structure in accordance with the AICPA Code of Professional Conduct and applicable law, regulations, and professional standards. CohnReznick LLP is a licensed CPA firm that provides attest services to its clients. CohnReznick Advisory LLC provides tax and business consulting services to its clients. CohnReznick Advisory LLC and its subsidiaries are not licensed CPA firms.
CohnReznick is an equal opportunity employer, committed to a diverse and inclusive team to drive business results and create a better future every day for our team members, clients, partners, and communities. We believe a diverse workforce allows us to match our growth ambitions and drive inclusion across the business. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, age, national origin, or protected veteran status and will not be discriminated against on the basis of disability. For more information, please see Equal Employment Opportunity Posters
If you are an individual with a disability in need of assistance at any time during our recruitment process, please contact us at [email protected] Please note: This email address is reserved for individuals with disabilities in need of assistance and are not a means of inquiry about positions or application statuses.
CohnReznick does not accept unsolicited resumes from third\-party recruiters unless such recruiters are currently engaged by CohnReznick Talent Acquisition Team by way of a written agreement to provide candidates for a specified opening. Any employment agency, person or entity that submits an unsolicited resume does so with the understanding that CohnReznick will have the right to hire that applicant at its discretion without any fee owed to the submitting employment agency, person or entity.
\#LI\-CM1 \#LI\-Remote \#GD \#IND123
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 CohnReznick, 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.
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.
CohnReznick AI Hiring
CohnReznick has 2 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Based in Austin, TX, US.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% above 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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