Note: The job is a remote job and is open to candidates in USA. The Home Depot is seeking a Senior Software Engineer to architect, develop, and scale enterprise-grade AI and agentic automation solutions across its IT and Marketing ecosystems. The role focuses on building multi-agent systems, industrializing AI use cases, integrating governance and observability guardrails, automating workflows, engineering RAG pipelines, evaluating AI systems, and mentoring engineers.
Responsibilities
- Agentic Framework Architecture: Designs, builds, and deploys scalable multi-agent systems and orchestration layers that power a flexible digital workforce capable of autonomous business planning, content generation, and execution
- Enterprise AI Scaling: Drives the technical execution of prioritized enterprise AI use cases, taking successful prototypes and rapidly industrializing them into stable, high-throughput production solutions across channels and platforms
- AI Foundation & Guardrail Integration: Implements core platform safety and performance layers, integrating model governance, comprehensive observability tracking, data protection, and ethical AI validation checks directly into the model lifecycle
- End-to-End Workflow Automation: Connects autonomous AI agents and subagents (e.g., Content Operations, Workflow Automation, and Analytics agents) with core enterprise databases and MarTech platform layers to completely eliminate manual process friction
- Model Optimization & RAG Engineering: Architectures robust Retrieval-Augmented Generation (RAG) pipelines, semantic caching, and vector database structures to ensure enterprise AI models remain context-aware, highly accurate, and performant
- Asynchronous Agent Evaluation: Develops advanced automated testing suites (including functional, regression, and destructive stress testing) tailored for non-deterministic AI outputs and complex multi-agent loop systems
- TechOps Automation Synergy: Partners occasionally with the internal TechOps support function to build self-healing automation loops, leveraging AI to enhance the IT organization's primary incident detection and automated triage capabilities
- Learns through successful and failed experiment when tackling new problems; Actively seeks ways to grow and be challenged using both formal and informal development channels
- Collaborates with other team members in agile processes; Creates new and better ways for the organization to be successful; Works the Product Team to ensure user stories are valuable, developer ready, easy to understand and testable; Delivers multi-mode communications that convey a clear understanding of the unique needs of different audiences; Adapts approach and demeanor in real time to match the shifting demands of different situations; Relates openly and comfortably with diverse groups of people
- Helps grow junior engineers by providing guidance on modern software development frameworks, and leading technical discussions
Skills
- • Must be eighteen years of age or older
- • Must be legally permitted to work in the United States
- • 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
- • 3
- • 3–6 years of professional software engineering experience, with a heavy emphasis on distributed systems, AI/ML application architecture, or intelligent workflow automation
- • Strong proficiency in scripting and object-oriented programming languages foundational to modern enterprise AI development (preferably Python, Java, or Go)
- • Direct hands-on experience building multi-agent systems or working with agent orchestration frameworks (e.g., LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel)
- • Deep technical understanding of Large Language Models (LLMs), prompt engineering paradigms, vector databases (e.g., Pinecone, Milvus, Chroma), and embedding techniques
- • Experience establishing AI Observability & Evaluation systems to track model drift, latency, costs, hallucination rates, and agent-to-agent performance (e.g., using LangSmith, TruLens, Phoenix)
- • Experience with MLOps pipelines and cloud-native AI infrastructures (AWS, GCP, or Azure AI ecosystems) for scaling model deployments and managing asynchronous workloads
- • Familiarity with enterprise data streaming, API management, and integration layers (e.g., connecting AI agents to CDPs, CRMs, and Content Management Systems)
- • Strong understanding of enterprise software design patterns, microservices architecture, and source code version control (Git)
- • Exposure to security frameworks, ethical AI guidelines, and regulatory model compliance (data governance, privacy protection) within corporate environments
- • Proven tracking record of breaking down complex, ambiguous business requirements into lean, high-impact technical architectures
- • Experience mentoring junior engineering talent and leading architectural design reviews across cross-functional technology teams
Company Overview