Senior Machine Learning Engineer – Generative AI
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Job Description
Senior Machine Learning Engineer building generative AI applications for The Home Depot. Developing LLM, RAG, and production AI solutions.
Responsibilities:
- Design, build, integrate, optimize, and maintain AI-powered applications using generative models
- Support the overall product lifecycle for generative AI products
- Collaborate with UX, engineering, product management, business stakeholders, infrastructure teams, development teams, and support teams
- Design and implement applications using large language models and other generative models
- Perform prompt engineering, model integration, RAG pipeline development, and scalable AI service development
- Evaluate, test, monitor, and optimize AI systems in production
- Work with domain data and improve prompts and AI workflows
- Create documentation and enablement materials for generative AI solutions
- Work independently with minimal guidance while collaborating with cross-functional teams
- Review code and prompt implementations and provide feedback based on engineering and responsible AI best practices
- Document, review, and ensure quality and change control standards are met
- Ensure user stories are developer-ready, understandable, and testable
- Write custom code or scripts to automate infrastructure, monitoring services, and test cases
- Perform destructive testing to ensure production resiliency
- Configure commercial off-the-shelf solutions for evolving business needs
- Create dashboards, logging, alerting, and proactive issue responses
- Field questions from product and support teams
- Provide application support for software running in production
- Monitor production Service Level Objectives
- Review performance and capacity across code, infrastructure, data, message processing, and prediction quality
- Report to a Software Engineer Manager or Senior Software Engineer Manager
- Maintain no direct reports
Requirements:
- Must be eighteen years of age or older
- Must be legally permitted to work in the United States
- Minimum education: high school diploma and/or GED
- Minimum 2 years of work experience
- Experience in Python and modern AI development frameworks
- Experience building Generative AI applications using large language models (LLMs)
- Experience with prompt engineering, prompt optimization, and prompt evaluation techniques
- Experience integrating AI models through APIs from platforms such as Google, OpenAI or Anthropic
- Experience with GenAI frameworks such as Google Agent Development Kit (ADK)
- Experience implementing Retrieval-Augmented Generation (RAG) pipelines using vector databases
- Experience working with vector databases such as Google Vertex AI Search
- Experience building conversational AI systems or AI assistants
- Experience with responsible AI practices, including bias mitigation and safety guardrails
- Experience with graph databases, knowledge ingestion pipelines, and data mesh architectures
- Experience implementing CI/CD pipelines, monitoring, and automated workflows for reliable AI model deployment and lifecycle management
- Experience with monitoring, evaluation, and optimization of production AI systems
- Experience in Google Cloud Platform and AI/ML components such as Vertex AI and BigQueryML
- Experience with data engineering practices and big data platforms such as BigQuery and Data Store
- Experience in a modern scripting language, preferably Python
- Experience with GPU acceleration such as CUDA and cuDNN
- Experience in front-end technologies and frameworks such as Node.js, HTML, CSS, JavaScript, ReactJS, and D3
- Experience writing SQL queries against a relational database
- Familiarity with production systems design, including High Availability, Disaster Recovery, Performance, Efficiency, and Security
- Familiarity with cloud computing platforms, automation patterns, and machine learning services
- Familiarity with defensive coding practices and patterns for high availability
- Familiarity with A/B testing and REST design for scalable web services architecture
- Familiarity with advanced machine learning techniques such as NLP, convolutional neural networks, autoencoders, and embeddings generation and utilization
Benefits:
- Remote/Virtual work arrangement
- Overnight travel typically required only 5% to 20% of the time
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