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About This Role
ABOUT US
HirexHire (pronounced hire by hire) is a recruiting and talent consultancy that integrates with companies short\-term to provide long\-term talent solutions. We take a seat in our client’s everyday operations to understand their people goals, gaps, and challenges. We then develop and implement the processes and technologies to execute a sustainable and scalable talent plan.
We partner with companies expecting or experiencing high growth who need to hire at scale or fill a critical role rapidly. Our clients are not looking for quick\-fix placements but are thoughtfully building a hiring strategy to scale their businesses.
OUR CLIENT
Location: Milwaukee, WI
Industry: E\-Commerce
Company Size: 20\+
What They Do: Our client provides an industry\-preferred platform that gives the power to team dealers, custom apparel decorators, \& corporate suppliers to launch online stores with confidence. With a heavy focus on faster site building, more accurate inventory management, consistent/automatic pricing, and on\-time/accurate fulfillment, our client’s goal is to make building sites easy.
THE ROLE
Our client is seeking an Internal Forward Deployed AI Engineer to build and deploy AI\-powered solutions that streamline operations, automate workflows, and improve decision\-making across the organization. Reporting directly to the CEO, this individual will partner with teams across the business to identify high\-impact opportunities and develop production\-grade AI agents, integrations, and automation solutions that connect existing systems and eliminate manual work. THis is a highly hands\-on engineering role for someone who enjoys solving complex business problems, working across functions, and turning emerging AI capabilities into measurable business outcomes.
WHAT YOU WILL DO
- Design, build and maintain AI agents, workflow automations, and internal tools that improve efficiency across the organization.
- Develop integrations between business\-critical platforms such as HubSpot, Microsoft 365, Slack, Confluence, and other SaaS practices.
- Partner with leadership and department stakeholders to identify workflow bottlenecks and deliver scalable solutions that drive measurable business impact.
- Build production\-ready applications and services using APIs, webhooks, AI technologies, and modern software engineering practices.
- Evaluate when AI\-driven solutions are appropriate versus when traditional software engineering or deterministic automation approaches are the better fit.
- Implement monitoring, evaluation, frameworks, and performance tracking to ensure the reliability, quality, and effectiveness of AI\-powered systems.
- Establish best practices for AI governance, data security, privacy, and responsible technology adoption.
- Research emerging AI tools, models, frameworks, and automation technologies to identify opportunities for innovation and operational improvement.
- Measure and communicate the business impact of deployed solutions, including efficiency gains, cost savings, and process improvements.
- Create and maintain technical documentation that supports long\-term scalability, maintainability, and knowledge sharing.
WHAT YOU WILL NEED
- 5\+ years of professional software engineering experience, including building and supporting production applications and integrations.
- Strong proficiency in Python, TypeScript, or another modern programming language used for AI and automation development.
- Experience integrating with third\-party APIs and business SaaS platforms such as HubSpot, Microsoft 365, Slack, Confluence, or similar systems.
- Hands\-on experience developing AI\-powered applications using large language models, including prompt design, retrieval workflows, tool use, evaluation strategies, and performance optimization.
- Experience building agent\-based systems, workflow orchestration solutions, or multi\-step automations that connect multiple platforms and services.
- Familiarity with leading AI platforms and model providers such as Claude, OpenAI, Gemini, Copilot, or similar technologies.
- Working knowledge of automation tools such as Zapier, Make.com, n8n, or comparable workflow platforms.
- Strong problem\-solving and debugging skills, with experience diagnosing issues across integrated and distributed systems.
- Knowledge of API security, secrets management, data governance, and best practices for handling sensitive business information.
- Excellent communication skills with the ability to translate business needs into technical solutions and effectively collaborate with both technical and non\-technical stakeholders.
- Self\-motivated, adaptable, and comfortable operating in a fast\-paced environment where priorities and business needs evolve over time.
- Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field preferred; equivalent professional experience will also be considered.
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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At HirexHire, 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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
HirexHire AI Hiring
HirexHire has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Milwaukee, WI, US.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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