Role Summary
Build multi-agent systems that turn data intelligence into automated, client-facing workflows. You will design and implement agentic architectures that make measurement models, data pipelines, and media optimization tools accessible through natural interfaces.
The goal: solutions that are cheap to build, cheap to run, and easy for non-technical teams to use.
Key Responsibilities
- Design and build multi-agent systems using LLM orchestration frameworks (LangChain, LangGraph, CrewAI, or custom)
- Implement AI orchestration use cases for 4+ clients, focusing on low external dependency scenarios
- Build agentic wrappers around existing data science models (MMM, forecasting, RBA, incrementality) so they can be queried and used conversationally
- Set up and configure Genie spaces for client data exploration and self-service analytics
- Develop tool-use patterns that connect agents to Databricks, Power BI, and other platform APIs
- Write production-grade Python code with proper error handling, logging, and monitoring
- Collaborate with Data Scientists to understand model inputs/outputs and build reliable agent interfaces
- Participate in architecture reviews and contribute to the team's agentic design patterns and reusable components
Required Qualifications
- 3+ years of software engineering experience with strong Python skills
- 1+ year hands-on experience building with LLMs (prompt engineering, RAG, function calling, agent frameworks)
- Experience with at least one agent orchestration framework (LangChain, LangGraph, AutoGen, CrewAI)
- Understanding of API design, microservices, and event-driven architectures
- Experience with cloud platforms (Azure preferred)
- Familiarity with vector databases, embeddings, and retrieval-augmented generation patterns
- Strong debugging and problem-solving skills in distributed systems
- Comfort working in a fast-moving team that ships iteratively
Preferred Qualifications
- Experience with Databricks and/or Genie Background in media, advertising, or marketing technology
- Experience building conversational interfaces or chatbot systems
- Familiarity with MLOps practices and model serving infrastructure Open-source contributions or personal projects demonstrating agentic AI work
