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About This Role
Req ID\#: 413626
Remote, Any Location, US
Job Description:
About Foundever:
Foundever™ is a global leader in the customer experience (CX) industry. With 170,000 associates across the globe, we are the team behind the best experiences for \+750 of the world’s leading and digital\-first brands. Our innovative CX solutions, technology and expertise are designed to support the operational needs of our clients and deliver a seamless experience to customers in the moments that matter.
Job Summary
We are looking for a commercially driven Director of AI Professional Services to lead our growing AI delivery practice. From prototyping to production, you will first need to assess and define the best way to address the client’s need to secure revenue, weather through the EverSuite capability, a custom solution (AI or not). If a custom AI solution is needed, you will lead its development. If the solution is effectively sold, you will oversee the transition to operationalization. At first, you will personally work with sales and one developer to discover, prototype, demo and deliver the solution. As demand grows, you will have to build the team, the delivery methodology and a sustainable operation model. This is a rare opportunity to build something from the ground up — with full commercial ownership, direct client impact, and the backing of a global AI\-powered organization.
Key responsibilities
Client Delivery \& Engagement
- Take a client brief from whiteboard to working demo within days, not weeks: personally prototype solutions in the first client engagements, before the team is built
- Design and deliver tailored AI solutions leveraging LLMs, agents, and AI\-assisted development workflows, from discovery through go\-live, favoring quick ROI but aligning with EverSuite roadmap
- Create repeatable offerings such as AI discovery workshop, rapid prototyping, paid pilots implementation packages and managed AI solution support.
- Oversee the transition of delivered solutions to production — covering monitoring, reliability, cost control, support models, and migration to standard offerings where appropriate
- Establish standards for responsible AI delivery, including data privacy, security, model selection, prompt/version management, monitoring and safeguards where required.
Commercial Ownership
- Own the P\&L for the professional services practice, including revenue targets and margin
- Partner with Sales to scope, price, and close new services opportunities
- Develop repeatable service packages, pricing models, and statements of work
- Identify expansion opportunities within existing accounts to grow ARR and services revenue
Practice Building
- Build, hire, and mentor a team of AI consultants, engineers, and solution architects, from scratch.
- Establish delivery methodologies, quality standards, and playbooks for AI implementations
- Work cross\-functionally with Product and Engineering to feed client insights back into the roadmap
WHAT WE'RE LOOKING FOR
Must\-Haves
- 7\+ years in professional services, consulting, or solutions delivery — ideally in an AI, data, or SaaS context
- Proven commercial track record: owned revenue targets, managed SOWs, and driven services P\&L
- Strong hands\-on fluency with AI assisted development workflow (Cursor, Copilot, Claude, v0, Replit, etc.) to prototype, scaffold, and ship working software rapidly, while maintaining engineering judgment around security, scalability, maintainablity and production readiness.
- Fluency in LLM application patterns: RAG, agents, fine\-tuning, prompt engineering, and tool use
- Ability to operate at the whiteboard with engineers and in the boardroom with C\-suite stakeholders
- Demonstrated ability to build and scale delivery teams in a fast\-moving environment
Nice\-to\-Haves
- Background in enterprise software, cloud platforms, or AI/ML infrastructure
- Experience selling or delivering services in regulated industries
- Startup or scale\-up experience — comfortable with ambiguity and building from scratch
- Familiarity with MLOps, vector databases, or AI safety considerations
WHAT 'VIBE CODING FLUENCY' MEANS HERE
This isn't a checkbox. We expect our Director to personally use AI\-assisted development tools — not just understand them conceptually. You should be able to spin up a working prototype with an LLM in hours, review AI\-generated code critically, and coach clients on integrating vibe coding into their own development culture.
You don't need to be a career software engineer. But you should be someone who has shipped things with these tools and has a point of view on where they accelerate delivery and where they introduce risk.
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 Foundever, 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. Director-level AI roles across all categories have a median of $247,800.
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.
Foundever AI Hiring
Foundever 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
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