Interested in this AI/ML Engineer role at Foundever?
Apply Now →Skills & Technologies
About This Role
Req ID#: 418391 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 building an AI Professional Services practice from the ground up. We need a technically savy Lead Architect who can own the build end-to-end: from client brief to working prototype, from prototype to production, and from solo contributor to the technical anchor of a growing team.
This is a rare founding opportunity. You will personally ship the first solutions, establish what great AI delivery looks like at Foundever, and grow into a Tech Lead role as the practice scales. You will work closely with Sales, Product, and client stakeholders — bringing engineering credibility and realistic delivery judgment to every engagement.
WHAT YOU'LL DO
1. Client Delivery & Technical Build
- Be the primary builder in every early client engagement — take a scoped brief to a working prototype within days, not weeks
- Design and deliver tailored AI solutions using LLMs, agents, RAG pipelines, and AI-assisted development workflows — from discovery through go-live
- Build live demos and proof-of-concepts that directly support deal closure, working alongside commercial stakeholders
- Own technical quality across all solutions: security, scalability, maintainability, and production readiness
- Lead the transition of delivered solutions to production — including monitoring, cost control, reliability, and support handoff
- Apply responsible AI delivery standards: data privacy, model selection, prompt and version management, and safeguards where required
2. Commercial & Scoping Judgment
- Assess client needs and determine the best solution path: EverSuite capability, off-the-shelf tooling, or custom AI build
- Estimate build costs with precision — time, infrastructure, tooling, and ongoing maintenance
- Join client scoping sessions and technical discovery calls, bringing engineering credibility and realistic timelines
- Flag reusable solution patterns that can evolve into packaged service offerings
- Partner with Sales to scope, price, and validate new services opportunities from a technical standpoint
3. Product Feedback Loop
- Identify recurring custom client requests that signal mid-term product opportunities
- Communicate technical insights clearly and actionably to Product Managers
- Define what "productizable" looks like technically — providing the feasibility foundation for roadmap decisions
- Feed client insights back into the EverSuite roadmap through structured cross-functional collaboration
4. Practice Building
- Document architecture patterns, reusable components, and build playbooks from day one
- Establish delivery methodologies and quality standards for AI implementations
- Create repeatable offerings: AI discovery workshops, rapid prototyping packages, paid pilots, and managed AI solution support
- As the team grows, step into a Tech Lead role — setting technical standards, reviewing work, and mentoring junior architects and engineers
- Define technical hiring criteria for future team members
WHAT WE'RE LOOKING FOR
Must-Haves
- 7+ years in solutions architecture, software engineering, or technical consulting — ideally in an AI, data, or SaaS context
- Strong hands-on track record building and shipping solutions — not just designing them
- Deep fluency with AI-assisted development workflows (Cursor, Copilot, Claude, v0, Replit, etc.) — you build with these tools daily
- Solid understanding of LLM application patterns: RAG, agents, prompt engineering, tool use, and fine-tuning
- Strong engineering judgment — you know when to move fast and when production readiness requires slowing down
- Ability to present your own work clearly to both technical teams and non-technical client stakeholders
- Proven ability to estimate, scope, and defend technical build decisions commercially
- Demonstrated ability to operate autonomously and lead without a fully formed team around you
Nice-to-Haves
- Background in enterprise software, cloud platforms, or AI/ML infrastructure
- Experience in a startup, scale-up, or zero-to-one environment — comfortable building without a playbook
- Familiarity with MLOps, vector databases, or AI safety considerations
- Prior experience influencing a product roadmap from field or client insights
- Exposure to P&L ownership, SOW management, or professional services revenue models
- Background in BPO, CX, or contact center technology
- Experience in regulated industries where responsible AI is non-negotiable
WHAT "AI BUILD FLUENCY" MEANS HERE
This isn't an architecture-on-paper role. We expect our Lead Architect to personally ship working software — not just produce diagrams and hand off to others. You use AI-assisted development tools as a force multiplier, not a crutch. You review AI-generated code critically, make real-time architecture decisions without committee alignment, and have a clear point of view on where these tools accelerate delivery and where they introduce risk.
You should be able to demonstrate all of the following:
- Spin up a working prototype within hours of a client brief
- Critically review AI-generated code — not just prompt and ship
- Make real-time architecture decisions without waiting for committee sign-off
- Scaffold, ship, and iterate on real solutions under commercial pressure
- Coach clients on integrating AI-assisted workflows into their development culture
- Maintain engineering rigor around security, scalability, and maintainability
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
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 $188,000 across 2,316 positions. About 6% 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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.