Senior Agentic Engineer

Job Description:

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

• 5+ years of software engineering experience with strong Python skills
• 2+ 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

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