Skills and Competencies

•  5+ years developing and maintaining production Python applications, with at least 2 years deploying machine learning models or data-intensive systems; must be comfortable navigating existing codebases, understanding architectural patterns, and building reliable, maintainable systems through clean, well-tested, deployable work that others can extend
• Strong experience with version control (Git), peer review workflows, continuous integration/deployment pipelines, environment management, and AI-assisted development tools; essential for contributing to a shared repository with multiple collaborators
•  Experience writing automated tests, building evaluation frameworks, diagnosing issues, and resolving problems independently; critical for ensuring reliability as capabilities evolve
• Understanding of large language model mechanics—context windows, token economics, prompt engineering, embeddings, model limitations, responsible use considerations, and approaches to evaluating output quality
•  Demonstrated ability to connect technical work to business outcomes; track record of building features informed by user needs and measurable impact
• Working knowledge of Databricks, Delta Lake, or similar Lakehouse environments; helpful for understanding how applications consume governed data assets.

Education

• Bachelor's degree required in Computer Science, Software Engineering, Data Science, or related technical field.

Responsibilities

This role focuses on extending a Python analytics platform, with involvement in LLM-powered features as capabilities mature. Candidates will contribute to a shared codebase and integrate features with existing systems, building toward ownership of AI-powered automation through hands-on implementation

• Contribute to agentic solutions capable of multi-step reasoning and tool use over sales data, including pipeline analysis automation, business review preparation, win/loss synthesis, and conversational interfaces for decision support
• Support retrieval and context engineering solutions that ground model responses in governed Sales data; work on embedding pipelines, chunking strategies, and search mechanisms that respect enterprise security requirements
•  Write comprehensive tests including unit tests and integration tests; develop evaluation approaches to measure output quality; ensure features meet reliability and governance standards before deployment
• Partner with business stakeholders to understand requirements and prioritize feature development; translate user needs into technical specifications
• Manage feature deployments through the team's environment promotion process; own production monitoring, alerting, and issue resolution to ensure reliability and performance standards.

About the Team

Our Sales Planning & Analysis team is responsible for the analytics, forecasting, and planning that Sales leadership uses to run the business globally. We deliver pipeline diagnostics, territory models, and business reviews that drive leadership decisions. By joining our team, you will build AI-powered capabilities that automate and enhance these workflows