Interested in this AI/ML Engineer role at Vontas?
Apply Now →About This Role
Job Summary:
Group AI Transformation Manager
The Group AI Transformation Manager is the primary driver of AI adoption across a group of 6\+ business units, reporting to the Group Leader and partnering closely with the Modaxo AI Centre of Excellence (CoE). This is fundamentally a change, commercial, and capability role — the human bridge between enterprise AI strategy and operational reality of each business unit. The AI Transformation Manager does not build the business unit’s AI solutions; they accelerate the pace at which AI is adopted, embedded, generates value driving solutions for our customers that are brought to market quickly and scaled across the group. Strong candidates will typically come from transformation, senior business units partnering backgrounds or commercial and technical strategy backgrounds. Remote candidates in North America are considered with regular travel expected.Job Description:
Job Summary:
================
The Group AI Transformation Manager is the primary driver of AI adoption across a group of 6\+ business units, reporting to the Group Leader and partnering closely with the Modaxo AI Centre of Excellence (CoE). This is fundamentally a change, commercial, and capability role — the human bridge between enterprise AI strategy and operational reality of each business unit. The AI Transformation Manager does not build the business unit’s AI solutions; they accelerate the pace at which AI is adopted, embedded, generates value driving solutions for our customers that are brought to market quickly and scaled across the group. Strong candidates will typically come from transformation, senior business units partnering backgrounds or commercial and technical strategy backgrounds. Remote candidates in North America are considered with regular travel expected.
Duties / Responsibilities:
==============================
AI Strategy \& Opportunity Development
------------------------------------------
- Develop and maintain an AI transformation roadmap for the group, mapping each business unit’s maturity, near\-term priorities, and 12–24 month opportunity pipeline aligned to Group Leader and business unit strategy.
- Identify and prioritize AI use cases using a structured framework (impact × feasibility × strategic fit) and lead regular AI opportunity reviews with Group Leader, business unit leaders, and other Modaxo Functions
- Translate market trends on emerging AI capabilities into specific commercial opportunities for business units.
AI Adoption \& Implementation Acceleration
----------------------------------------------
- Act as the primary implementation partner for business units deploying AI tools — removing adoption barriers and moving from decision to live deployment faster.
- Lead AI pilot scoping, design, and monitoring to generate transferable learning and scale\-ready outcomes; replicate successful use cases across business unit s.
- Hold B business unit leaders accountable for agreed AI adoption milestones with appropriate support and challenge.
AI Champions Partnership \& Capability Business development
---------------------------------------------------------------
- Serve as day\-to\-day partner to AI Champions — providing guidance, escalation support, and a link to other Modaxo resources.
- Help Champions develop business unit AI readiness assessments and activation plans; identify capability gaps and coordinate learning interventions.
- Track and report AI literacy levels and adoption readiness using a consistent maturity framework.
Stakeholder Engagement \& Change Leadership
-----------------------------------------------
- Build relationship as a trusted advisor with business unit leaders; manage the human side of AI change proactively and ensure no business unit is left behind.
- Brief the Group Leader regularly on adoption progress, emerging risks, and priority decisions; prepare a monthly AI adoption summary for upward reporting.
- Support talent and role evaluation to ensure business unit s have the right skills to deliver AI strategy; connect AI adoption to the AI Impact Personas framework.
Performance, Measurement \& Reporting
-----------------------------------------
- Own the group’s AI adoption scorecard: adoption rates, use cases live, value delivered, capability development, and maturity progression.
- Conduct periodic AI maturity assessments and provide regular reporting to the Group Leader and other Modaxo stakeholders against the transformation roadmap.
- Other duties as assigned.
Education and Experience:
=============================
- Demonstrable track record leading complex, multi\-business unit change programs — ideally technology\-related.
- Background in management consulting, business transformation, change management, digital program leadership, or senior commercial roles with significant change exposure in a software development or technology area.
- Evidence of translating strategy into practical action through people who do not report to them; experience coaching and enabling others.
- Minimum 5 years of progressive experience in transformation, change, or strategic business unit partnering roles.
- Post\-secondary degree in Business Administration, Commerce, Organizational Behavior, or related field preferred.
- Desirable: direct AI deployment experience, multi\- business unit /group structure background, familiarity with ADKAR/Kotter or AI maturity frameworks, and Modaxo/CSI or relevant sector experience.
Physical Requirements:
==========================
- Prolonged periods working at a desk and on a computer or laptop.
- Ability to travel up to 30–40% of the time (domestic and international) to business sites.
- Must be able to lift up to 15 pounds at a time and handle high utilization of hand and wrist dexterity.
Disclaimers:
================
All job requirements are subject to possible revision to reflect changes in the position requirements or to reasonably accommodate individuals with disabilities. Some requirements may exclude individuals who pose a threat or risk to the health and safety of themselves or other employees.
This job description in no way states or implies that these are the only duties required in this position. Employees will be required to follow other job\-related duties as requested by their supervisor/manager (within guidelines and compliance with Federal and State Laws). Continued employment remains on an “at\-will” basis.
Worker Type:
RegularNumber of Openings Available:
1
We thank all applicants for their interest; however, only those who qualify for an interview will be contacted. \*Professional recruiting agents or consultants need not call.
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 Vontas, 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 $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.
Vontas AI Hiring
Vontas has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.