Senior Software Engineer - Marketing Technology (Remote)

The Home Depot•GEORGIA - VIRTUAL - GA01, GA

6d•$80,000 - $180,000•Remote

About The Position

The Senior Software Engineer for Applied AI Acceleration is responsible for the technical architecture, development, and scaling of enterprise-grade AI and agentic automation solutions designed to drive operational efficiency across the broader IT and Marketing ecosystems. Rather than focusing on isolated software refactoring, this position leads the construction of our "Digital Workforce Ecosystem"—a flexible, multi-agent operating model that orchestrates autonomous AI subagents across the entire campaign lifecycle, from insights and planning to execution and real-time optimization. As a Senior Engineer, you will transition high-value AI use cases into production-ready platform capabilities, scaling agentic workflows across enterprise channels and platforms. You will be responsible for ensuring all AI systems are built on a rock-solid operational foundation, embedding core enterprise guardrails—including model governance, strict AI observability, data privacy, and ethical AI frameworks—directly into the production stack. Additionally, you will collaborate with cross-functional IT and business teams, mentoring engineers of all experience levels to foster a culture of rapid AI exploration, evaluation, and delivery.

Requirements

Must be eighteen years of age or older.
Must be legally permitted to work in the United States.
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.
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.

Nice To Haves

No additional education
No additional years of experience
None

Responsibilities

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.
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.
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.
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.
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.
Develops advanced automated testing suites (including functional, regression, and destructive stress testing) tailored for non-deterministic AI outputs and complex multi-agent loop systems.
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.