Sr. Software Engineer - Internal Apps

About the role

We’re building a new function dedicated to full-stack tools and AI-powered services for GTM, Finance, Support, and Product. You’ll work on greenfield internal applications that surface data for decision-making and automate operational processes. While early prototypes exist, the mandate is to define and build out this product portfolio. You’ll own the applications—frontend, backend, deployment, and model integration—while data and analytics engineers handle the underlying data platform.

Responsibilities

• Design, build, and operate full-stack web apps (FastAPI/Flask + React/TypeScript today, though technology choices are open) that deliver data and AI capabilities to stakeholders for decision support and operational work.
• Integrate AI/LLM features—classification, extraction, summarization, copilots, and agentic workflows—using the best models, providers, and frameworks for the problem.
• Deploy and operate applications on GCP (App Engine, Cloud Run, GKE), manage auth, CI/CD, and app security.
• Define the product vision for this new function: prioritize problems for custom apps vs. BI dashboards, establish reusable building blocks, and ensure reliable, observable services.
• Collaborate with stakeholders to scope solutions, work with analytics engineers on data models, and align with data engineers on platform constraints.

Requirements

• 5+ years building production software, with significant full-stack web application experience.
• Strong Python skills: APIs (FastAPI, Flask, or similar), data access patterns, packaging, and testing.
• TypeScript/React (or comparable framework) for component design and interactive data UIs.
• Hands-on experience with GCP application services (App Engine, Cloud Run, GKE, IAM).
• Strong SQL and comfort working with cloud data warehouses (e.g., BigQuery)—writing queries, understanding costs, and designing data access layers.
• Experience developing and deploying AI/LLM-powered applications in production: prompt design, structured output, evaluation, cost/latency tradeoffs, and adaptability to evolving models/tooling.
• Experience operating production systems: logging, monitoring, error handling, and debugging.
• Familiarity with software engineering best practices: CI/CD, automated testing, observability, and secure application design.
• Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience.

Nice to Have

• Experience building AI-native applications: text-to-SQL interfaces, copilots, agentic workflows, or automated insight-generation systems.
• Hands-on experience with LLM provider APIs (Anthropic’s Claude, OpenAI, Google, open-weight models) and agent frameworks (Claude Agent SDK, LangGraph, or similar).
• Experience with managed AI/ML platforms (Vertex AI, SageMaker): model serving, embeddings, evaluation tooling.
• Familiarity with dbt and modern data warehouse patterns from a consumer’s perspective.
• Experience with Airflow for triggered jobs and background work.
• Familiarity with Terraform for managing application infrastructure.
• Background designing data-heavy UIs: tables, drill-downs, large result sets, interactive exploration.
• Prior experience as the first or only application engineer on a data team, owning the full lifecycle.