Note: The job is a remote job and is open to candidates in USA. DataDirect Networks (DDN) is a global market leader in AI and high-performance data storage innovation. The Senior Data / AI Application Engineer will design and build internal applications on DDN’s enterprise data platform, focusing on AI-powered services and full-stack tools to enhance operational processes and decision-making.
Responsibilities
- Design, build, and operate full-stack web apps (FastAPI/Flask + React/TypeScript today, but technology choices are open) that put data and AI into stakeholders’ hands — both as decision-support interfaces and as purpose-built tools that let them do operational work
- Build features powered by LLMs and ML — classification, extraction, summarization, copilots, agentic workflows — choosing whichever models, providers, and frameworks fit the problem
- Deploy and operate apps on GCP (App Engine, Cloud Run, GKE), connect them to the data platform, manage auth, own CI/CD and app security
- Define what good looks like for this new function: which problems are worth a custom app vs. a BI dashboard, what our reusable building blocks should be, and how we ship reliable, observable services people depend on
- Partner with stakeholders to scope the right tool for the job, with analytics engineers to shape the underlying data models, and with data engineers on platform constraints
Skills
- 5+ years building production software, with meaningful time spent on full-stack web applications
- Strong Python — APIs (FastAPI, Flask, or similar), data access patterns, packaging, testing
- TypeScript/React (or comparable framework), component design, 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 (BigQuery in our case) — you can write a query, understand its cost, and design an app's data access layer around it
- Experience developing and deploying AI/LLM-powered applications in production — prompt design, structured output, evaluation, cost/latency tradeoffs, awareness that the model and tooling landscape changes quickly
- Experience operating what you ship — logging, monitoring, error handling, debugging in production
- Experience with software engineering best practices: CI/CD, automated testing, observability, secure application design
- Bachelor's degree in Computer Science, Engineering, or equivalent practical experience
- Experience building AI-native applications such as text-to-SQL interfaces, copilots, agentic workflows, or automated insight-generation systems
- Hands-on experience with one or more LLM provider APIs (Anthropic's Claude, OpenAI, Google, open-weight models, etc.) and agent frameworks (Claude Agent SDK, LangGraph, or similar)
- Experience with managed AI/ML platforms (Vertex AI, SageMaker, or similar) — 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 — comfort owning the full lifecycle
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