Interested in this AI/ML Engineer role at PCS Wireless GLOBAL?
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About This Role
Ready to be a part of a game\-changing team that thrives on defying the impossible?
About PCS Wireless:
Founded in 2001, by two visionary traders, PCS Wireless, affectionately known as “PCS”, is not your average mobile distributor. Led by fearless entrepreneurs, PCS has completely transformed the landscape of the device resell market, both from a business and a consumer perspective.
Today, PCS is a recognized global leader, powering the secondary market. At PCS, we buy and sell mobile devices and products worldwide through partners and programs by breathing new life into old devices effectively extending the device lifecycle up to 5X and beyond. We collaborate with industry giants in consumer electronic manufacturing, wholesalers, big box retailers and small businesses alike, catering to a diverse clientele of more than 1,500 clients. Our operations span major markets worldwide with offices and warehouses in the Americas, APAC, UK \& EMEA.
Our go\-getting spirit valuing flexibility, a "me for we approach" and curiosity, continues to be the foundation of our success. We are looking for doers and thinkers who get things done and have fun while doing it!
Job Description:
PCS is in the middle of an AI transformation. Our technology team is growing roadmap of automation across Operations, Sales, Finance, and beyond.
We're hiring a Forward Deployed AI Engineer to drive that roadmap into production across every department. Reporting into Solutions \& Delivery leadership, you'll embed with Operations, Sales, Pricing, Finance, Commercial, and Engineering running discovery, training teams to use AI in their day\-to\-day, and building and shipping the solutions yourself.
This is a high\-ownership execution role. The roadmap is set with leadership; you own delivery against it.
Success is measured by production adoption of the agents and tools you ship, measurable workflow impact (hours saved, errors reduced, cycle time cut), and the number of PCS team members operating fluently with AI as part of their job.
Job Responsibilities:
- Embed with departments. Spend real time with Operations, Sales, Pricing, Finance, Commercial, and others. Shadow workflows, map manual processes, and translate them into clear technical scopes.
- Build and ship AI solutions end\-to\-end. Execute against the prioritized roadmap — scope, design, build, and deploy agents and integrations on top of our existing MCP, ERP, SQL, and Power BI stack. Prototype fast, harden for production, measure impact.
- Train and enable. Run hands\-on sessions to make every engineer, analyst, and operator capable of solving real problems with AI. Build internal playbooks, prompt libraries, and reusable patterns.
- Partner with department leaders. Be the trusted technical contact each department turns to when they want to apply AI to a workflow. Translate their input into well\-scoped deliverables that fit the broader roadmap.
- Follow established quality standards. Build agents that are evaluable, observable, and safe. Contribute to and reinforce the patterns the team has set for how AI is deployed at PCS.
- Close the loop. Feed learnings from deployed agents back to leadership and the platform team so the roadmap and underlying infrastructure keep improving.
Who You Are:
- 4–7 years of software engineering experience, including hands\-on work with LLMs, AI agents, or generative AI in production (not just demos).
- Stakeholder\-facing instinct. You like sitting with non\-technical users, asking the right questions, and turning vague pain into shipped systems.
- Full\-stack builder. Comfortable writing production code, designing data pipelines, integrating with ERPs (NetSuite a plus), and standing up front\-end interfaces when needed.
- Strong execution. You consistently take a scoped initiative from intake through production adoption, on your own initiative, without needing handholding.
- Teaching instinct. You enjoy explaining what you built, why it works, and how others can do the same.
- Bias to ship. You'd rather have something in production this week than a perfect plan next quarter.
Nice to have:
- Experience with MCP (Model Context Protocol), agent orchestration frameworks, RAG pipelines, or eval\-driven AI development.
- Background in supply chain, wireless, distribution, refurb, or reverse\-logistics workflows.
- Exposure to NetSuite, SQL, Power BI, Jira, Confluence.
- Have built and rolled out internal AI tooling that was actually adopted (not just demoed).
We Are Seeking People Who:
- Act like owners.
- Are continually raising the bar.
- Are sincerely open\-minded and willing to examine their strongest convictions with humility.
- Nurture and embrace differing perspectives to make better decisions.
What's in it for You:
- Competitive salary and performance\-based incentives.
- Opportunities for professional growth and development.
- A supportive and collaborative work environment.
- Comprehensive benefits package.
*In alignment with pay transparency requirements, the salary range for this role is $100,000 to $150,000 annually. Final compensation may vary based on factors such as experience and qualifications. PCS Wireless offers a robust benefits package designed to support the health, well\-being, and financial security of our employees. Specific offerings may vary depending on role, start date, and employment type.*
*We are an Equal Opportunity Employer. All qualified applicants for employment without regard to race, color, religion, sex, gender, sexual orientation, gender identity, ancestry, age, or national origin will be considered. No qualified applicants will be discriminated against on the basis of disability or protected veteran status.*
Salary Context
This $100K-$150K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →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 PCS Wireless GLOBAL, 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. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($125K) sits 31% below the category median. Disclosed range: $100K to $150K.
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.
PCS Wireless GLOBAL AI Hiring
PCS Wireless GLOBAL has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Florham Park, NJ, US. Compensation range: $150K - $150K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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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