Responsibilities
- 65% 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; Program configuration/modification and setup activities on large projects using HD approved methodology; 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
- 15% 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
- This Position typically reports to Software Engineer Manager or Sr Software Engineer Manager
- This Position has 0 Direct Reports
- Typically requires overnight travel 5% to 20% of the time.
- 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.
- Located in a comfortable indoor area. Any unpleasant conditions would be infrequent and not objectionable.
Basic qualifications
- Must be eighteen years of age or older.
- Must be legally permitted to work in the United States.
Preferred qualifications
- 1–3 years of relevant work experience in Generative AI, Machine Learning, or AI application development 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 Familiarity with building conversational AI systems, or AI assistants Familiarity with responsible AI practices including bias mitigation and safety guardrails Familiarity with REST APIs, microservices architecture, and scalable AI system deployment Familiarity implementing CI/CD pipelines, monitoring, and automated workflows for reliable AI model deployment and lifecycle management. Familiarity with monitoring, evaluation, and optimization of production AI systems
- The knowledge, skills and abilities typically acquired through the completion of a high school diploma and/or GED.
- No additional education
- 1
- No additional years of experience
- None
- None
- None
- Global Perspective
- Manages Ambiguity
- Nimble Learning
- Self-Development
- Collaborates
- Cultivates Innovation
- Situational Adaptability
- Communicates Effectively
- Drives Results
- Interpersonal Savvy
Tags & focus areas
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