Senior AI Engineer – Forward Deployed, Agent Platform (REMOTE)

The Home Depot; United States; Remoto; USD 100.000 - 180.000; Tempo pieno

Vantaggi offerti da questo lavoro

Health care benefits

401K plan

ESPP

Paid time off

Descrizione del lavoro

The Home Depot is seeking an experienced Software Engineer to embed with business verticals and deliver enterprise-grade AI agents. You will partner with Finance, Marketing, Supply Chain, Online, and Store Ops to translate operational processes into governed AI solutions.

You will build end-to-end agents using Python (FastAPI), Gemini Enterprise, and Google ADK, with HITL confirmation gates and secure REST integrations to enterprise systems.

Competenze

• 6+ yrs shipping/operating production Python services (FastAPI, async microservices, REST).
• Multi-step agent orchestration with LLM APIs (Gemini, Google ADK).
• Systematic prompt engineering, structured outputs, context optimization & evaluation.
• Enterprise SaaS & MCP integration with auth boundaries (OAuth2, SSO, RBAC).
• RAG, knowledge ingestion & search capabilities.

Mansioni

• Embed with THD Business Verticals and turn operational processes into enterprise-governed agents.
• Build and deploy production agents using Python, Google ADK, and Gemini Enterprise with HITL gates.
• Develop MCP servers and REST integrations for Jira, Confluence, SharePoint/M365, Slack, ServiceNow, Salesforce.
• Create reusable evaluation harnesses and deployment templates to accelerate future vertical builds.
• Review submissions for quality and governance against engineering, security, and Responsible AI standards.

Formazione

High school diploma or GED

Strumenti

Jira

Confluence

SharePoint/M365

Descrizione del lavoro

With a career at The Home Depot, you can be yourself and also be part of something bigger.

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.

For California, Colorado, Connecticut, Rhode Island, Nevada, New York City, Ithaca (NY), Westchester County (NY), and Washington residents:

The pay range for this position is between $100,000.00 - $180,000.00