Interested in this AI/ML Engineer role at TheSoul Group?
Apply Now →About This Role
TheSoul Group is a global media company with over a decade of experience building and operating digital businesses at scale. With a presence across 60 platforms, content distributed in 21 languages, and more than 100 billion monthly views, we have established one of the largest and most operationally sophisticated media footprints in the world. Our remote\-first team, spanning 85\+ countries, has grown by solving — at real scale — the exact organisational and operational challenges most companies struggle with.
That operational depth is the foundation of Valis — TheSoul Group's brand new business and AI platform for enterprise transformation. Valis is an institutional AI operating layer for mid\-market organisations (roughly 200–2,000 employees). It sits across a company's systems, runs one shared, org\-wide "brain" on everything the company knows, and applies encoded operator judgment on every answer — so the value of AI compounds for the company, not just for individuals using per\-seat chatbots.
Valis is already proven in production — built from real transformation work, encoding operational experience rather than theory — and we are now scaling its deployment to mid\-market companies worldwide.
We are seeking a Client Deployment Lead, AI to embed on\-site alongside mid\-market CEOs and make their AI\-Native transformation real on the ground. You work as a tight pod: you on\-site owning the CEO relationship and the result and you drive the rollout to a finance\-verified outcome, with elite remote engineers shipping the product configuration behind you. Every CEO who watches you land the transformation becomes the proof that wins the next one. This role is the engine of our go\-to\-market.
Responsibilities* Own the client\-CEO relationship and the executive steering committee — as a peer, with real decision rights.
- Run the transformation cadence: weekly reviews, monthly finance\-verified value reviews, and quarterly business reviews.
- Drive a fast, fixed\-scope first win on the client's real data, then lead the full rollout (leadership first, then the wider team).
- Translate client problems into crisp, build\-ready specs for remote engineers; own adoption and sign\-off.
- Be personally accountable for a finance\-verified outcome the client can see in their P\&L.
- Build a portfolio of 2–3 marquee CEO accounts as each engagement matures.
- Own end\-to\-end rollout management — from kick\-off through adoption, sign\-off, and handover — ensuring every phase lands on time and to a measurable outcome.
- Codify the method after each deployment so the next one requires less ramp\-up.
Requirements
- + A track record of owning real outcomes — you have moved a P\&L or shipped a transformation, not just advised on one.
+ CEO\-room presence: ability to sit across from a mid\-market CEO as a peer and earn organisational trust quickly.
+ Technically literate enough to prototype, spec work, and direct engineers — you shape the solution, you don't wait for one.
+ You thrive in ambiguity — operating effectively with incomplete information, shifting requirements, and no fixed playbook.
+ Willing and energised to be on\-site with clients (\~30–60% travel).
+ Likely background: top\-tier strategy firm (Engagement Manager Associate Partner) or a proven in\-house transformation operator.
+ Hands\-on experience with AI / data tooling is a strong plus.
Benefits
- Compensation: A competitive base calibrated to your market and seniority, plus a performance bonus tied to delivered outcomes.
- Equity: Meaningful, founder\-tier ownership — this is the real upside, and it's sized to match.
- Authority: Real decision rights and an interim\-executive mandate inside the client where granted. You own the result.
- Trajectory: A portfolio of marquee CEO relationships and a clear path to Regional GM.
- Location: On\-site with the client during deployment (\~30–60% travel across the US); your home base is flexible.
- Why AI makes this seat more valuable, not less: Valis automates the coordination, reporting and status layer of a company. This role sits on the other side of that line: CEO trust, judgment under ambiguity, and accountability for a real outcome are exactly what don't automate. As the platform gets stronger, the seat compounds in leverage — it doesn't erode.
We appreciate your interest in our job opportunities and our company. Our team carefully evaluates each application to identify the most suitable candidates for the role. Due to the high volume of applications received, we may not be able to respond to every applicant. However, if your qualifications align with our requirements, we will contact you to discuss the next steps in the selection process.
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 TheSoul Group, 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. Senior-level AI roles across all categories have a median of $227,400.
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.
TheSoul Group AI Hiring
TheSoul Group has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in 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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