Interested in this AI/ML Engineer role at Catch Intelligence?
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CATCH Intelligence is seeking an experienced Data Governance Practice Lead Consultant to lead and grow its Data Governance Practice with a focus on Data and AI Governance. This position combines practice leadership, client delivery, people leadership, and business development. The Data Governance Practice Lead serves as a trusted advisor to executive clients, leads complex governance engagements, manages the Data Governance consulting team, partners with Sales to develop new business, and continually evolves CATCH's methodologies, accelerators, and intellectual property.
Key Leadership Responsibilities
- Lead the continued growth and evolution of CATCH's Data Governance Practice, including developing and continuously enhancing Data and AI Governance service offerings, reusable methodologies, frameworks, strategic accelerators, templates, standards, and intellectual property.
- Lead and serve as the functional manager for the Data Governance consulting team, including coaching, mentoring, career development, performance management, resource planning, and practice leadership.
- Lead the continued integration of AI Governance into CATCH's Data Governance practice, including maturation of AI governance frameworks, responsible AI guidance, AI policies and standards, AI risk management approaches, and AI adoption strategies for clients.
- Partner with executive leadership to define and execute strategic initiatives that expand CATCH's Data Governance and AI Governance capabilities and market presence.
- Collaborate with the Sales Team on opportunity qualification, solution strategy, proposal development, statements of work, client presentations, and executive\-level discovery sessions.
- Serve as a trusted advisor to executive sponsors by facilitating governance strategy discussions, organizational alignment, and executive decision\-making around enterprise data and AI initiatives.
- Participate in practice planning, revenue growth initiatives, utilization planning and management, and continuous improvement efforts.
- Lead organizational change management efforts that drive sustainable adoption of Data Governance, Data Quality, Data Stewardship, Master Data Management, Metadata Management, and AI Governance practices.
- Monitor emerging trends, technologies, and industry best practices related to Data Governance, AI Governance, Data Management, Analytics Governance, and Responsible AI, incorporating relevant advancements into CATCH's methodologies and client offerings.
- Support the development and delivery of marketing collateral; thought leadership content; conference presentations, webinars, white papers, and practice\-related publications; executive, business, and technical education, workshops, and governance enablement sessions.Client Delivery Responsibilities
- Lead client enterprise assessments, governance strategies, roadmaps, and implementation programs.
- Facilitate executive steering committees, governance councils, and stakeholder workshops.
- Design governance operating models including stewardship, policies, standards, issue management, metadata, business glossary, and data quality.
- Develop and maintain reusable consulting assets, implementation accelerators, templates, standards, and best practices.
- Lead pilot initiatives and phased governance implementations that demonstrate measurable business value and establish a foundation for enterprise adoption.
- Guide organizational change management and adoption.
- Advise executives on governance priorities, maturity, risks, and investments.
- Lead implementation of Data Governance, Data Quality, Metadata Management, and AI Governance capabilities appropriate to each client's maturity and business objectives.
- Tailor CATCH Data Governance method to each client's objectives and maturity.
- Lead development of Data Governance and AI Governance proposals, statements of work, estimates, and client solution strategies.Business Development \& Sales Support
- Partner with Sales on opportunity qualification, discovery, solution strategy, proposals, SOWs, and estimates.
- Participate in executive sales meetings and conference presentations.
- Identify follow\-on opportunities and strengthen long\-term client relationships.People Leadership
- Serve as functional manager for the Data Governance Consulting Team.
- Support recruiting, interviewing, and onboarding new Data Governance consultants.
- Coach, mentor, and develop consultants to promote long\-term governance success and organizational self\-sufficiency.
- Support resource planning, utilization, knowledge sharing, and performance development and management.Qualifications
- 8\+ years demonstrated experience leading enterprise Data Governance programs from strategy through operational implementation.
- Experience developing executive\-level governance strategies, roadmaps, operating models, and organizational change management initiatives.
- Experience supporting business development activities, including pre\-sales, proposal development, executive presentations, and solution estimation.
- Experience leading consulting teams and mentoring Data Governance professionals.
- Strong understanding of AI Governance concepts, Responsible AI principles, AI risk management, and enterprise AI adoption strategies.
- Working knowledge of the Data Management Association (DAMA) Data Management Body of Knowledge (DAMA\-DMBOK) and its application to enterprise Data Governance, Master Data Management, Data Quality, Metadata Management, Data Stewardship, and related data management disciplines.
- Certified Data Management Professional (CDMP) certification is preferred or demonstrated commitment to professional development in enterprise Data Management and Data Governance best practices.
- Experience working across multiple industries with executive stakeholders, business leaders, and technical teams.
- Knowledge of Data Governance platforms such as Collibra, Informatica, Microsoft Purview, Alation, SAP, or similar technologies is preferred.
- Excellent communication, presentation, leadership, facilitation, negotiation, and relationship management skills.
- Proven ability to build trusted advisor relationships with C\-level executives and senior business leadership.
- Bachelor's degree or equivalent experience.
Note: This is a full\-time salaried position. MUST be able to travel 30% or more!
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 Catch Intelligence, 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 in Demand for This Role
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. Senior-level AI roles across all categories have a median of $230,000.
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.
Catch Intelligence AI Hiring
Catch Intelligence has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Omaha, NE, US.
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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