The Home Depot
The Home Depot

Senior AI Engineer – Forward Deployed, Agent Platform

$100–180kAtlanta, GARemoteSeniorFull-time

Posted 2 days ago

The role in short

Embed with business verticals to build, deploy, and govern production-ready AI agents and enterprise integrations using Python and Google's AI stack.

Position Purpose

  • Embed with THD Business Verticals: Partner directly with Finance, Marketing, Supply Chain, Online, Store Ops, HR, and Legal/Risk, etc., to turn ambiguous operational processes into production-ready, enterprise-governed AI agents.
  • Educate & Enable: Teach verticals what surfaces they can deploy on (Gemini Enterprise, Google ADK, Skills, Agent Designer, MCP servers, Canvas, Agent Gateway/Registry) and how to manage and evolve their agents. Consult business on technical approaches to build agents.
  • Drive Adoption & Reuse: Grow Gemini Enterprise adoption across the enterprise – team agents, individual Sparks, and a marketplace of reusable enterprise agents, Skills and tools. Treat every engagement as supply for the marketplace and actively promote reuse so teams compose existing assets before building new ones.
  • Explicit Scope Guardrail: Foundation models are consumed as managed services—no pre-training/fine-tuning models, no GPU/CUDA kernel work, and no classical feature stores or model registries.

Responsibilities

  • Embed & Discover Workflows: Sit with non-technical business partners to elicit requirements, define negative scope ("what the agent must never do"), and lock in acceptance criteria before coding.
  • Build & Deploy Production Agents: Ship end-to-end multi-step agents and conversational assistants in Python (FastAPI), Google ADK, and Gemini Enterprise, including Human-in-the-Loop (HITL) confirmation gates for write actions.
  • Engineer Enterprise Integrations & MCP Servers: Build resilient MCP servers and REST integrations for enterprise systems (e.g. Jira, Confluence, SharePoint/M365, Slack, ServiceNow, Salesforce, etc.) with strict OAuth/IAM boundaries, idempotency, retries, and High Availability patterns; publish them as reusable assets in the enterprise marketplace.
  • Establish Reusable Evaluation Harnesses: Build workflow-level golden datasets, prompt/agent evaluations, and automated regression suites so business owners have an objective pass/fail gate.
  • Templatize & Build the Paved Road: Extract reusable templates, Skills, and packaging standards from every build so the second vertical build takes ≤60% of the effort of the first.
  • Review Submissions & Govern Quality: Review partner-submitted agent code, company Skills, and prompt implementations for the Agent Sharing & Registry workflow against engineering, security, and Responsible AI standards;curate the marketplace so published agents, Skills and tools are discoverable, documented and trusted.
  • Execute Ownership Handoff & Enablement: Author runbooks and enablement materials, verify receiving-owner readiness, and cleanly transfer day-to-day operations to the vertical.
  • Feed Platform Architecture: Convert recurring vertical needs into scoped backlog items for the core APEX platform (Gateway/Registry, Model Armor guardrails, observability, and lifecycle management).
  • Enable Builders & Grow Adoption: Support product to run technical workshops, office hours; review low-code agents built by business teams; maintain documentation and a pattern library; and use usage data and feedback to improve adoption and the platform.

Key Responsibilities

  • 70% Delivery and Execution - Collaborates and pairs with other product team members (UX, engineering, and product management) to create secure, reliable, scalable machine learning solutions; Documents, reviews, and ensures that all quality and change control standards are met; Works with Product Team to ensure user stories that are developer-ready, easy to understand, and testable; Writes custom code or scripts to automate infrastructure, monitoring services, and test cases; Writes custom code or scripts to do "destructive testing" to ensure adequate resiliency in production; Configures commercial off the shelf solutions to align with evolving business needs; Creates meaningful dashboards, logging, alerting, and responses to ensure that issues are captured and addressed proactively
  • 10% Learning - Participates in learning activities around modern software design, machine learning, and development core practices (communities of practice); Proactively views articles, tutorials, and videos to learn about new technologies and best practices being used within other technology organizations
  • 20% Support and Enablement - Fields questions from other product teams or support teams; Monitors tools and participates in conversations to encourage collaboration across product teams; Provides application support for software running in production; Proactively monitors production Service Level Objectives for products; Proactively reviews the Performance and Capacity of all aspects of production: code, infrastructure, data, message processing, and prediction quality

Direct Manager/Direct Reports

  • This Position typically reports to Software Engineer Manager or Sr. Software Engineer Manager
  • This Position has 0 Direct Reports

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.

Preferred Qualifications

  • 6+ yrs shipping/operating production Python services (FastAPI, async microservices, REST)
  • Multi-step agent orchestration (Google ADK, LangGraph, CrewAI, AutoGen, or equivalent) & LLM APIs (Gemini, Claude, OpenAI)
  • Systematic prompt engineering, structured outputs, context optimization & evaluation
  • Enterprise SaaS & MCP integration + auth boundaries (OAuth2, SSO, service accounts, delegated user, RBAC)
  • RAG, knowledge ingestion & search (Vertex AI Search / Gemini Enterprise Data Stores, access-scoped retrieval)
  • Agent evaluation harnesses, golden datasets & HA/defensive coding practices
  • GCP (Vertex AI, Cloud Run, BigQuery), analytical SQL, CI/CD, and observability (Grafana, Prometheus, OpenTelemetry)
  • Forward-deployed execution: cross-functional collaboration across skill levels, code/prompt review, enablement docs & handoff
  • Customer-facing delivery & communication: demonstrated experience working with nontechnical stakeholders, comfort with ambiguity, and presenting technical tradeoffs to executives (assessed in the interview via a mock discovery session).
  • Security & Responsible AI fundamentals for agents that take write actions: PII handling, HITL approvals, audit trails, and least-privilege access.
  • Direct hands-on experience with Gemini Enterprise, Vertex AI Search / Data Stores, Google ADK, and MCP / FastMCP
  • Prior Forward Deployed, Solutions, or Embedded Platform Engineer experience
  • HITL approval workflows, write-action safety controls & immutable audit trails
  • Responsible AI & safety guardrails in prod (Model Armor, prompt-injection defense, bias/hallucination mitigation, PII redaction)
  • A/B testing, graph/connected knowledge systems, Terraform/HCL, Docker/GKE, and TypeScript/React/JS for UI/Canvas surfaces
  • Retail, eCommerce, supply chain, store ops, or finance domain background

Minimum Education

  • The knowledge, skills and abilities typically acquired through the completion of a high school diploma and/or GED.

Preferred Education

  • No additional education

Minimum Years Of Work Experience

  • 2

Preferred Years Of Work Experience

  • No additional years of experience

Minimum Leadership Experience

  • None

Preferred Leadership Experience

  • None

Certifications

  • None

Competencies

  • Global Perspective
  • Manages Ambiguity
  • Nimble Learning
  • Self-Development
  • Collaborates
  • Cultivates Innovation
  • Situational Adaptability
  • Communicates Effectively
  • Drives Results
  • Interpersonal Savvy

Benefits offered include health care benefits, 401K, ESPP, paid time off, and success sharing bonus. For a full list of the various benefits The Home Depot offers, visit https://careers.homedepot.com/our-benefits.

Prep for this interview

5 questions likely to come up at The Home Depot, each matched to a line in this posting.

  1. 1Discovery and scoping
    They wrote: “Embed & Discover Workflows: Sit with non-technical business partners to elicit requirements, define negative scope ("what the agent must never do"), and lock in acceptance criteria before coding.”

    A customer says “we want AI in our claims process.” What do you ask in the first meeting?

    How to answer

    Why they ask: Most FDE work starts this vague. They want to hear you find the real problem before you pick a tool.

    1. Map the current process: who touches a claim, in what order, with which systems
    2. Find the slowest or most error-prone step and ask what it costs them
    3. Agree on one measurable goal (for example time per claim, or error rate)
    4. Ask what data you can access, and who signs off on security

    Tip: Ask about the work, not the technology. Who does it today, how long it takes, what a mistake costs, and how they'd know it got better.

  2. 2Integrations and APIs
    They wrote: “Enterprise SaaS & MCP integration + auth boundaries (OAuth2, SSO, service accounts, delegated user, RBAC)”

    Walk me through connecting our product to a customer's internal system that has no documentation.

    How to answer

    Why they ask: This happens weekly in the job. They want a calm, step-by-step method.

    1. Find who owns the system and get read access to a test environment
    2. Watch real traffic or logs to learn the data shapes
    3. Build a thin adapter with logging around every call
    4. Agree on how failures get reported, and who fixes what

    Tip: Talk about finding the owner of the system first. The code is the easy part.

  3. 3Cloud, deployment and security
    They wrote: “A/B testing, graph/connected knowledge systems, Terraform/HCL, Docker/GKE, and TypeScript/React/JS for UI/Canvas surfaces”

    How would you debug a deployment that works in staging but fails at the customer's site?

    How to answer

    Why they ask: Customer environments differ in ways you can't see. This tests method under pressure.

    1. Compare versions, config and environment variables
    2. Check network rules, proxies and certificates (common in enterprise)
    3. Look at resource limits and permissions
    4. Add the missing check to your install steps afterwards

    Tip: List the usual suspects and how you'd check each without full access.

  4. 4Data and SQL
    They wrote: “GCP (Vertex AI, Cloud Run, BigQuery), analytical SQL, CI/CD, and observability (Grafana, Prometheus, OpenTelemetry)”

    Write a query that finds each customer's first and most recent order.

    How to answer

    Why they ask: A quick check that you're comfortable with grouping, joins and window functions.

    1. Clarify the tables and what counts as an order (cancelled ones?)
    2. Use MIN/MAX with GROUP BY, or ROW_NUMBER() if you need the whole row
    3. Handle ties on the same timestamp, and customers with no orders
    4. Explain how you'd test it on a few known customers

    Tip: Say what you'd check in the data before writing the query: duplicates, time zones, nulls.

  5. 5LLMs, RAG and agents
    They wrote: “Explicit Scope Guardrail: Foundation models are consumed as managed services—no pre-training/fine-tuning models, no GPU/CUDA kernel work, and no classical feature stores or model registries.”

    The model gives a confident wrong answer to an important customer. What now?

    How to answer

    Why they ask: Tests how you handle trust, on top of the technical fix.

    1. Tell the customer the same day what you know and when they'll hear more
    2. Reproduce it and find the cause (bad retrieval, missing data, prompt, model)
    3. Add the case to the test set so it can't come back unnoticed
    4. Add a guard where it matters: citations, confidence checks, human review

    Tip: Own it with the customer first, then show the change that stops it happening again.

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The Home Depot

$100–180k · Remote

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