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Position Purpose:
The Sr Machine Learning Engineer is responsible for designing, building, integrating, optimizing, and maintaining AI-powered applications that leverage generative models and the overall product lifecycle for a product that our users love. Generative AI Engineers are expected to collaborate closely with teammates as they develop and deliver user stories while supporting AI-powered products as they evolve. Generative AI Engineers may design and implement applications using large language models (LLMs) and other generative models to embed intelligent capabilities directly into software products. Activities may include prompt engineering, model integration, building Retrieval-Augmented Generation (RAG) pipelines, and developing scalable AI services. The role may interact with business stakeholders, infrastructure teams, and development teams to ensure business requirements are effectively addressed through generative AI solutions. The role may also support evaluation, performance optimization, testing, and monitoring of AI systems in production. Additional responsibilities may include working with domain data, improving prompts and AI workflows, and creating documentation or enablement materials for generative AI solutions.
Sr Generative AI Engineers should be able to work independently with minimal guidance, while collaborating with cross-functional teams of varying skill levels to design, deploy, and maintain production AI applications. This role may review submitted code and prompt implementations, providing feedback and improvements based on engineering and responsible AI best practices.
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:
- 3 - 5 years of relevant 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 with building conversational AI systems, or AI assistants
- Experience with responsible AI practices including bias mitigation and safety guardrails
- Experience working with graph databases, knowledge ingestion pipelines, and data mesh architectures to enable scalable, connected, and queryable AI knowledge systems.
- 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 related components such as Vertex AI, BigQueryML, and Experience in effective data engineering practices and big data platforms such as BigQuery, Data Store, etc- Experience in a modern scripting language (preferably Python)
- Experience with GPU acceleration (i.e. CUDA and cuDNN)
- Experience in a front-end technology and framework such as Node.js, HTML, CCS, JavaScript, ReactJS, D3
- Experience in 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 platform and associated automation patterns and machine learning services they provide
- Familiarity with defensive coding practices and patterns for high Availability
- Familiarity with A/B testing and effective 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
Minimum Education:
- The knowledge, skills and abilities typically acquired through the completion of a high school diploma and/or GED.
Preferred Education:
Minimum Years of Work Experience:
Preferred Years of Work Experience:
- No additional years of experience
Minimum Leadership Experience:
Preferred Leadership Experience:
Certifications:
Competencies:
- Global Perspective
- Manages Ambiguity
- Nimble Learning
- Self-Development
- Collaborates
- Cultivates Innovation
- Situational Adaptability
- Communicates Effectively
- Drives Results
- Interpersonal Savvy