AI Applied Engineer
Redis
Who we are
We're Redis. We built the product that runs the fast apps our world runs on. (If you checked the weather, used your credit card, or looked at your flight status online today, you’re welcome.) At Redis, you’ll work with the fastest, simplest technology in the business—whether you’re building it, telling its story, or selling it to our 10,000+ worldwide customers. We’re creating a faster world with simpler experiences. You in?
Why You’ll Love This Role
You’ll join an influential, high-autonomy engineering team whose work powers Redis’ most visible AI integrations. One sprint you may publish a new LangGraph connector; the next you’ll pair with a Fortune 100 customer to harden a pilot RAG service. Your work ships in the open (on GitHub), in conference workshops, and inside customer proofs of concept—so you’ll drive immediate company revenue while also building a strong public portfolio. Come join an incredible team, ecosystem, and company working on the product that millions of developers love.
What You’ll Do
Build & Maintain AI-Ecosystem Integrations
Design, implement, and release open-source SDKs, plugins, and reference apps that marry Redis with agent frameworks (LangGraph, AutoGen, etc.).
Triage GitHub issues, review community PRs, and guard automated test coverage.
Evolve pipelines for linting, integration testing, and PyPI/NPM publishing.
Drive Customer Implementations
Embed with strategic customers for short POCs or pilots; transform lessons learned into public recipes and example projects.
Help bridge the gap between ideation and business value by leveraging GenAI.
Collaborate Cross-Functionally
Work shoulder-to-shoulder with Redis Pre-Sales Engineering on technical evaluations, with Product Marketing and DevRel on launch content, and with ecosystem partners to co-design solutions.
Close the Feedback Loop
Channel real-world performance findings, bleeding edge techniques, and feature requests back to Redis product and core-engineering teams.
What You’ll Need
Production GenAI Experience — You’ve shipped agentic or RAG systems in Python using LangGraph, AutoGen, or a similar framework.
Demonstrated Engineering Fundamentals — Comfortable with Git workflows, code reviews, automated testing, and containerization.
Open-Source Track Record — Demonstrable contributions through code, docs, or issue triage.
Communication Skills — Ready to explain code internals to engineers, design choices to architects, and walk non-technical leaders through a demo with targeted business value.
Self-Direction & Ownership — You set goals, unblock yourself, communicate well with the team, and surface risks early—no micromanagement required.
Nice-to-Haves
Previous experience in a customer-facing engineering or solutions-focused role.
Familiarity with Redis modules (Search, JSON, Gears) or other high-throughput datastores.
Exposure to cloud-native ops (AWS, GCP, Azure) and infrastructure-as-code.
TypeScript, Java, or Rust skills for multi-language demo stacks
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