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About This Role
A position at White Cap isn’t your ordinary job. You’ll work in an exciting and diverse environment, meet interesting people, and have a variety of career opportunities.
The White Cap family is committed to Building Trust on Every Job. We do this by being deeply knowledgeable, fully capable, and always dependable, and our associates are the driving force behind this commitment.
Job Summary
Responsible for leading a team of engineers at all levels, to operationalize data science models and solutions. Ensures that data science initiatives are effectively transformed into scalable, production-ready systems, and to develop engineering solutions that support and enhance data-driven capabilities across the organization.
Major Tasks, Responsibilities and Key Accountabilities
- Leads, mentors, and manages a team of engineers, fostering a collaborative and innovative environment. Oversee performance evaluations and support the professional development of team members to ensure high productivity and morale.
- Responsible for leading the deployment of data science models into production environments, ensuring scalability, reliability, and efficiency. The manager oversees the transformation of prototype models into production-ready solutions, implementing best practices for continuous integration and continuous deployment (CI/CD).
- Designs and develops engineering solutions that support data science initiatives. Collaborates closely with data scientists to translate models into robust, scalable systems and build infrastructure that enhances the organization's data-driven capabilities.
- Provides technical guidance on software engineering and machine learning practices, the manager ensures code quality, maintainability, and adherence to industry standards. Stay updated on emerging technologies and methodologies to incorporate best practices into the team's workflows.
- Works closely with data science, IT, and product teams to align engineering efforts with organizational goals. Communicates complex technical concepts to non-technical stakeholders, facilitating collaboration across departments to drive project success.
- Manages computing resources, cloud services, and infrastructure necessary for machine learning operations, optimizes resource utilization and cost efficiency. Ensures the team has the necessary tools and environments to perform effectively.
Nature and Scope
- Solutions require analysis and investigation.
- Achieves planned results by decisions and actions based on professional methods, business principles, and practical experience.
- Manages a group or team of professional individual contributors and/or indirectly supervises support staff.
Work Environment
- Located in a comfortable indoor area. Any unpleasant conditions would be infrequent and not objectionable.
- Most of the time is spent sitting in a comfortable position and there is frequent opportunity to move about. On rare occasions there may be a need to move or lift light articles.
- Typically requires overnight travel less than 10% of the time.
Education and Experience
- Typically requires BS/BA in a related discipline. Generally 7+ years of experience in a related field. May require certification. Advanced degree may offset less experience in some disciplines.
Preferred Qualifications
- 3 years' experience in a managerial or leadership role overseeing engineering or software-based teams.
- Proven track record of successfully delivering engineering and production solutions that are scalable and sustainable.
- Expertise in Python.
- Knowledge of big data technologies and platforms like Hadoop, Spark, and Databricks.
- Experience in managing complex projects involving cloud infrastructure, CI/CD, and model monitoring.
- Strong background in collaborating with cross-functional teams and effectively communicating complex technical concepts to non-technical stakeholders.
- Staying current with industry trends and emerging technologies in machine learning is expected to ensure the team remains at the forefront of innovation.
- Proficiency in cloud infrastructure, deployment pipelines, and software engineering.
- Proficiency in cloud platforms such as Microsoft Azure and Amazon Web Services (AWS).
- Advanced degree in Computer Science, or related field preferred.
- This is a hybrid role based in Doraville, GA.
If you’re looking to play a role in building America, consider one of our open opportunities. We can’t wait to meet you.
Functional Area Marketing and Communications
Work Type Remote
Recruiter Hampton, Corey
Req ID WCJR-030286
White Cap is an Equal Opportunity Minority/Female/Individuals with Disabilities/Protected Veteran and Affirmative Action Employer. White Cap considers for employment and hires qualified candidates without regard to age, race, religion, color, sex, sexual orientation, gender, gender identity, national origin, ancestry, citizenship, protected veteran or disability status or any factor prohibited by law.
Role Details
About This Role
This role sits at the intersection of AI and engineering, building systems that bring machine learning capabilities into production environments. The scope varies by company, but the common thread is applying AI technology to solve real business problems at scale. Most AI roles today require a combination of software engineering fundamentals and domain-specific ML knowledge, with the exact mix depending on the team's maturity and the product they're building.
The AI job market is evolving fast. New role categories emerge as companies figure out what they need to ship AI-powered products. What matters most is the ability to learn quickly, build working systems, and iterate based on real-world performance data. The specific title matters less than the skills you bring and the problems you can solve. Companies are past the experimentation phase and want engineers who can deliver production-quality systems that work reliably at scale.
Across the 37,339 AI roles we're tracking, AI Engineering Manager positions make up 0% of the market. At White Cap, this role fits into their broader AI and engineering organization.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
What the Work Looks Like
Day-to-day work involves a mix of building, debugging, and collaborating. You'll write code, review pull requests, participate in design discussions, and work with cross-functional teams (product, design, data) to define what AI features should do and how they should behave. Expect to spend time on both technical implementation and communication. Most AI teams operate in two-week sprint cycles, with regular demos and retrospectives. The ratio of heads-down coding to meetings and reviews varies by seniority, with senior roles spending more time on architecture decisions and mentorship.
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
Skills Required
Python and cloud platform experience are common requirements. Specific skill needs vary by company and focus area, but familiarity with ML frameworks, data pipelines, and API design covers the basics for most roles. RAG (Retrieval-Augmented Generation), vector databases, and LLM API integration are increasingly standard requirements across role types.
Beyond the core stack, communication skills matter more than many technical candidates realize. The ability to explain AI capabilities and limitations to non-technical stakeholders is a differentiator at every level. Technical writing, documentation, and clear thinking about tradeoffs are underrated skills in AI roles. Experience with evaluation methodology (how to measure whether an AI system is working well) is becoming a core requirement, especially for roles that involve LLM integration.
Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
Compensation Benchmarks
AI Engineering Manager roles pay a median of $293,500 based on 21 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $147,000.
Across all AI roles, the market median is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. For comparison, the highest-paying categories include AI Safety ($274,200) and Research Engineer ($260,000). By seniority level: Entry: $85,000; Mid: $147,000; Senior: $225,000; Director: $230,600; VP: $248,357.
White Cap AI Hiring
White Cap has 1 open AI role right now. They're hiring across AI Engineering Manager. Based in Atlanta, GA, US.
Location Context
Across all AI roles, 7% (2,732 positions) offer remote work, while 34,484 require on-site attendance. Top AI hiring metros: New York (1,633 roles, $204,100 median); Los Angeles (1,356 roles, $179,440 median); San Francisco (1,230 roles, $240,000 median).
Career Path
Common paths into AI Engineering Manager roles include Software Engineer, Data Scientist, Data Analyst.
From here, career progression typically leads toward Senior Engineer, AI Architect, Engineering Manager, Principal Engineer.
Focus on building things that work. A deployed project that solves a real problem is worth more than any certification. Contribute to open-source, build portfolio projects, and invest in fundamentals (software engineering, statistics, systems design) rather than chasing the latest framework. The AI field moves fast, but the engineers who succeed long-term are the ones with strong fundamentals who can adapt to new tools and paradigms as they emerge.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Look for job postings that specify the problems you'll work on, the tech stack, and the team structure. Vague postings that list every AI buzzword are often a sign the company hasn't figured out what they need. Strong postings describe the product context, the team you'd join, and the specific challenges you'd tackle.
AI Hiring Overview
The AI job market has 37,339 open positions tracked in our dataset. By seniority: 3,672 entry-level, 23,272 mid-level, 7,048 senior, and 3,347 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (2,732 positions). The remaining 34,484 roles require on-site or hybrid attendance.
The market median for AI roles is $190,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $300,688. Highest-paying categories: AI Engineering Manager ($293,500 median, 21 roles); AI Safety ($274,200 median, 24 roles); Research Engineer ($260,000 median, 264 roles).
AI hiring keeps growing across industries. Companies in tech, finance, healthcare, and retail are all building AI teams. The strongest demand is for people who can bridge the gap between AI research and production engineering. The shift toward generative AI has created new role types (LLM Engineer, Prompt Engineer, AI Agent Developer) that didn't exist three years ago, while traditional roles (Data Scientist, ML Engineer) have evolved to incorporate LLM capabilities.
The AI Job Market Today
The AI job market spans 37,339 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (33,926), AI Software Engineer (823), AI Product Manager (805). 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 (3,672) are outnumbered by mid-level (23,272) and senior (7,048) 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 3,347 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (2,732 positions), with 34,484 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 $190,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $300,688. 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 $145,600. 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: Rag (23,721 postings), Aws (12,486 postings), Rust (10,785 postings), Python (5,564 postings), Azure (3,616 postings), Gcp (3,032 postings), Prompt Engineering (2,112 postings), Kubernetes (1,713 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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