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About This Role
Position Purpose
As a Software Engineer Manager, you will lead teams that build cloud platform products enabling and supporting agentic workloads at The Home Depot. Your products serve a broad audience: engineering teams building, deploying, and running agentic workloads on our core platform, and non-engineering teams who need a secure, governed place to build and run their own applications and agentic solutions. You will be accountable for the delivery of these products, turning platform strategy into a roadmap, guiding your engineers through design, build, and release, and making sure every user, technical or not, can go from idea to running application with the least friction possible. Your teams will deliver experiences that meet users where they are, from simple guided deployment to command-line tooling and AI-assisted guidance, and that accelerate rapid prototyping and an AI-driven product development lifecycle.
You will partner closely with Platform Reliability Engineering to build support, observability, and reliability into these products from the start, so they are ready for production, on-call, and scale. You will also grow the next generation of engineering talent and build a strong platform-engineering culture.
Key Responsibilities
30% Delivery & Execution
Owns delivery of cloud platform products supporting agentic workloads, from roadmap and design through release and ongoing operation Collaborates with product management, UX, architecture, and consuming teams (both engineering and non-engineering) to create secure, reliable, scalable platform capabilities Partners with Platform Reliability Engineering to define SLOs, observability, alerting, runbooks, and on-call readiness for every product before and after launch Ensures quality, security, and change control standards are met; documents and reviews designs and releases Guides the design and build of services on Google Cloud and GKE, using infrastructure as code and CI/CD to deliver in small, safe increments Drives adoption of both platforms through clear documentation, self-service workflows, and guardrails tailored to each audience: paved paths for engineers and simple, governed experiences for non-technical users Establishes security, governance, and cost guardrails that let non-engineering teams build and run workloads on the platform safely Writes code or scripts as needed to automate infrastructure, monitoring, and testing; participates in design and code reviews Participates in and leads review board sessions to drive consistency across the enterprise
10% Support & Enablement
Fields questions from engineers, product teams, and support teams consuming the platform Serves as a technical escalation point for the team and participates in incident response and post-incident reviews Monitors adoption and feedback channels and turns them into product improvements Provides application support for software running in production
50% People
Provides leadership, mentoring, and coaching to Software Engineers Attracts, retains, and develops top talent to build a world class Software Engineering Team Conducts annual and mid-year reviews by reviewing individual development plans and team feedback Manages team capacity, priorities, and workload across multiple products Fosters collaboration with team members to drive consistency across product teams, and finds opportunities to expose engineers to career interests Acts as a proponent of modern software development practices, including applying AI-assisted development to how the team works Participates in and contributes to communities of practice around modern software design and development
10% Learning
Learns, through reading, tutorials, and hands-on experimentation, new technologies and best practices in cloud platforms, agentic AI, and platform engineering Builds relationships with technology leaders at other companies to learn best practices and elegant solutions to common problems
Direct Manager / Direct Reports
2-5
Travel Requirements
Typically requires overnight travel 5% to 20% of the time.
Physical Requirements
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.
Working Conditions
Located in a comfortable indoor area. Any unpleasant conditions would be infrequent and not objectionable.
Minimum Qualifications
Must be eighteen years of age or older Must be legally permitted to work in the United States Mastery of an object oriented programming language (preferably Go) Proficiency with Google Cloud Platform and Kubernetes (GKE) in a production environment
Preferred Qualifications
5-7 years of relevant work experience, including experience leading or managing engineers Leadership experience and a strong technology mindset Mastery of Golang for building cloud-native services, APIs, and tooling Mastery of Google Cloud (e.g., GKE, IAM, networking, Cloud Run, Pub/Sub, Cloud Storage) and container orchestration with Kubernetes Mastery of infrastructure as code and GitOps practices (e.g., Terraform, Helm, Argo CD) Mastery of modern CI/CD and progressive delivery pipelines Experience building platforms or products that enable agentic and AI workloads, such as LLM gateways, agent runtimes, tool and MCP integration, model serving, or AI orchestration on cloud infrastructure Experience designing platforms, APIs, and self-service workflows with excellent documentation and user experience, including for non-technical users Proficient in observability practices: metrics, logging, tracing, SLOs, and alerting (e.g., OpenTelemetry, Prometheus, Grafana) Proficient in production support, incident management, and building reliability into products in partnership with SRE or reliability engineering teams Proficient in security fundamentals for cloud platforms, including identity, secrets management, and network policy Proficient in effective troubleshooting and issue resolution techniques Capable of understanding complicated systems quickly Experience in guiding more junior team members through Software Engineering fundamentals in a professional setting Experience managing and growing team members in a professional setting Experience balancing workloads across teams and managing multiple products Experience with translating high level strategy to tactical execution AI driven thinking, including applying AI and agentic workflows to accelerate engineering Proficiency in supporting a 24x7 retail operation
Education
Minimum: the knowledge, skills and abilities typically acquired through the completion of a bachelor's degree program or equivalent degree in a field of study related to the job. Preferred: no additional education.
Years of Experience
Minimum: 5
Leadership Experience
5-7 years of relevant work experience, including experience leading or managing engineers
Certifications
None. (Google Cloud Professional certifications are a plus.)
Competencies
Attracts Top Talent: Attracting and selecting the best talent to meet current and future business needs Balances Stakeholders: Anticipating and balancing the needs of multiple stakeholders Builds Effective Teams: Building strong-identity teams that apply their diverse skills and perspectives to achieve common goals Business Insight: Applying knowledge of business and the marketplace to advance the organization's goals Collaborates: Building partnerships and working collaboratively with others to meet shared objectives Communicates Effectively: Developing and delivering multi-mode communications that convey a clear understanding of the unique needs of different audiences Develops Talent: Developing people to meet both their career goals and the organization's goals Drives Engagement: Creating a climate where people are motivated to do their best to help the organization achieve its objectives Drives Vision and Purpose: Painting a compelling picture of the vision and strategy that motivates others to action Manages Ambiguity: Operating effectively, even when things are not certain or the way forward is not clear Organizational Savvy: Maneuvering comfortably through complex policy, process, and people-related organizational dynamics Situational Adaptability: Adapting approach and demeanor in real time to match the shifting demands of different situations
Salary Context
This $140K-$240K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At The Home Depot, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($190K) sits 10% below the category median. Disclosed range: $140K to $240K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
The Home Depot AI Hiring
The Home Depot has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Atlanta, GA, US, Atlanta, GA. Compensation range: $240K - $240K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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