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AI Transformation & Change Manager
Company Overview
ActiveProspect is on a mission to make consent-based marketing the best channel for online customer acquisition. We provide marketers the products they need to acquire qualified customers at scale. Our platform is trusted by thousands of companies engaged in direct-to-consumer marketing, helping them save wasted spend, comply with ever-changing regulations, and manage a constantly evolving partner landscape. Our flagship product, TrustedForm, is used to certify over 1 billion opt-in digital customer leads every year and is the gold standard for documenting prior express written consent for TCPA compliance.
Job Summary
ActiveProspect is seeking an AI Transformation & Change Manager who will lead the practical adoption of artificial intelligence across the company’s non-technical functions. This is a highly hands-on role responsible for identifying high-value opportunities, designing and implementing AI-enabled solutions, managing organizational change, and building AI knowledge and confidence across functional teams.
This individual will translate the company’s AI strategy into measurable business outcomes. They will work directly with teams to understand existing processes, challenge the status quo, redesign workflows, deploy solutions, and ensure employees have the training and support necessary to adopt new ways of working.
The successful candidate will be both a strategic thinker and a strong executor. This is not a strategy-only or advisory role; the AI Transformation & Change Manager will be expected to personally drive initiatives from opportunity identification through implementation, adoption, and ROI measurement.
Responsibilities:
Internal AI Strategy and Roadmap Execution
- Partner with the CFO and the Vice President of Business Systems, Compliance and IT to develop and execute the company’s enterprise AI strategy staying aligned to the technical side of the house.
- Translate strategic priorities into a practical, sequenced AI roadmap with clear owners, milestones, dependencies, and success measures.
- Lead the company’s AI Strategy Team, including establishing meeting agendas, facilitating decisions, tracking commitments, and maintaining accountability for results.
- Monitor emerging AI capabilities and recommend practical applications that support the company’s business objectives.
- Balance speed, value, scalability, compliance, and organizational readiness when prioritizing AI initiatives.
- Own the AI tooling budget: forecast spend, evaluate cost against realized value, and recommend consolidation or termination of underperforming tools.
Opportunity Identification and Process Transformation
- Work directly with Finance, Accounting, Legal, HR, Revenue Operations, Customer Support, Implementations, Customer Success, Sales and other non-technical functions to identify opportunities for AI-enabled automation and improvement.
- Evaluate existing processes, challenge established ways of working, and identify opportunities to reduce manual effort, improve quality, accelerate decision-making, and enhance employee and customer experiences.
- Facilitate workshops and working sessions to uncover business needs and translate them into actionable AI use cases.
- Prioritize opportunities based on expected business value, implementation effort, risk, data availability, and likelihood of adoption.
- Develop clear business cases and recommendations for proposed AI initiatives.
Solution Design and Implementation
- Lead AI initiatives from initial concept through solution design, testing, implementation, adoption, and ongoing improvement.
- Partner with Business Systems, IT, Compliance, functional leaders, vendors, and other subject-matter experts to develop practical AI-enabled solutions.
- Personally drive execution, including documenting requirements, redesigning workflows, coordinating pilots, testing solutions, resolving implementation issues, and managing rollout plans.
- Establish repeatable processes and playbooks that allow successful AI solutions to scale across the organization.
- Track adoption and solution performance after implementation and make adjustments based on employee feedback and business results.
Change Management, Training and Adoption
- Develop and execute change-management plans that accelerate AI adoption and help employees successfully transition to new tools and workflows.
- Build AI fluency across non-technical teams through training, coaching, demonstrations, office hours, practical use cases, and ongoing communications.
- Help employees understand how AI can enhance their work while setting realistic expectations regarding its capabilities and limitations.
- Identify and support AI champions within functional teams who can reinforce adoption and share effective practices.
- Address resistance to change by understanding employee concerns, engaging leaders, and demonstrating measurable value.
- Create reusable educational materials, prompt libraries, implementation guides, and other resources that make AI accessible to employees with varying levels of technical knowledge.
Stakeholder Alignment and Governance
- Build alignment among executive leaders, functional teams, Business Systems, IT, and Compliance regarding AI priorities, responsibilities, and expected outcomes.
- Coordinate closely with the Compliance team to ensure AI programs are consistent with company policies, regulatory requirements, data privacy obligations, security standards, and responsible AI principles.
- Incorporate appropriate controls, documentation, human review, and monitoring into AI-enabled workflows.
- Communicate progress, risks, decisions, and results clearly to executive sponsors and other stakeholders.
- Ensure teams remain aligned throughout implementation and that issues requiring leadership decisions are escalated promptly.
Business Impact and ROI Measurement
- Establish baseline metrics and success criteria before beginning each AI initiative.
- Document and report the tangible and intangible value created by AI programs across each functional team.
- Measure outcomes such as time savings, cost reductions, productivity improvements, faster cycle times, increased capacity, improved accuracy, better decision-making, enhanced employee experience, and reduced risk.
- Develop dashboards and executive reporting that show implementation progress, adoption levels, realized benefits, and areas requiring additional attention.
- Conduct post-implementation reviews to confirm whether anticipated benefits were achieved and identify lessons for future initiatives.
- Maintain an enterprise view of the cumulative return on the company’s AI investments.
Qualifications and Skills
Preferred Qualifications
- Formal change-management certification or framework experience (Prosci / ADKAR or equivalent).
- Experience in a high-growth SaaS or private-equity-backed environment, and comfort operating without established process.
Key Competencies
- Hands-on execution and ownership
- Strategic thinking with strong operational discipline
- Curiosity and continuous learning
- Business-process redesign
- Change leadership and organizational influence
- Stakeholder management and executive presence
- Practical problem-solving
- Training, coaching, and facilitation
- Data-driven decision-making
- Sound judgment regarding risk and compliance
- Ability to challenge the status quo constructively
- Ability to create momentum in an evolving and ambiguous environment
Measures of Success
- The number and business value of AI solutions successfully deployed.
- Demonstrable efficiency, capacity, quality, and cost improvements.
- Adoption and sustained usage of AI tools and redesigned workflows.
- Growth in AI knowledge and confidence across non-technical teams.
- Completion of roadmap initiatives on schedule.
- Effective partnership and alignment among functional leaders, Business Systems, IT, and Compliance.
- Appropriate governance and compliance controls embedded in AI solutions.
- Clear documentation of tangible and intangible ROI by function.
- The company’s ability to scale successful AI practices across multiple teams.
Reports to:
- VP of IT and Business Systems
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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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At ActiveProspect, 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 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
ActiveProspect, Inc. AI Hiring
ActiveProspect, Inc. has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Austin, TX, US.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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