AI Systems Architect

About AppLovin

AppLovin makes technologies that help businesses of every size connect to their ideal customers. The company provides end-to-end software and AI solutions for businesses to reach monetize and grow their global audiences. For more information about AppLovin visit: .

To deliver on this mission our global team is composed of team members with life experiences backgrounds and perspectives that mirror our developers and customers around the world. At AppLovin we are intentional about the team and culture we are building seeking candidates who are outstanding in their own right and also demonstrate their support of others.

Fortune recognizes AppLovin as one of the Best Workplaces in the Bay Area and the company has been a Certified Great Place to Work for the last four years (2021-2024). Check out the rest of our awards HERE.

About the role

We are building a layered AI intelligence system a multi-layer agent architecture with a dynamic router context engine execution loop verification layer and eval feedback cycle. This system will be designed to handle long-horizon business tasks that cannot be accomplished in a single model inference.

As the AI Systems Architect you will own the design of the entire system. You will decide how intelligence is structured across layers how context flows between components when the system routes to a human and how failures feed back into improvement. You will set the standards the rest of the team builds to.

What you will build

• The router: the component that selects which role to invoke next executor planner verifier clarifier based on current task state risk level and uncertainty

• The context engine: RAG pipelines structured memory MCP tool connections and the prompt library that gives the system company-specific knowledge

• The execution loop: the action-observe-act cycle that lets the system pursue multi-step goals with real-world grounding

• The verification layer: checker model design confidence thresholds and human escalation logic for irreversible actions

• The eval loop: the feedback cycle that makes the system improve over time without retraining the base model

What we are looking for

• Deep understanding of how LLMs behave fail and can be steered through prompt design and context structure

• Experience building and shipping multi-step agentic systems in production not prototypes

• Systems thinking: ability to design interfaces between components reason about failure modes and make architecture decisions that scale

• Strong opinions about what does and does not work in LLM system design backed by real experience

• Comfort with ambiguity this is a new field and there are no established playbooks

Nice to have

• Experience with RAG architectures vector databases and retrieval systems

• Familiarity with MCP LangChain LlamaIndex or similar agentic frameworks

• Background in distributed systems or backend infrastructure

• Experience designing eval systems and benchmarks for LLM outputs