Lead Engineer, AI & Business Intelligence

Overview

About Ovative Group
Ovative Group is an independent, full-funnel media, measurement, and creative firm. Leveraging our deep industry expertise, we help brands like Best Buy, Domino's, American Eagle, The Home Depot, Post, Disney, Tumi, Michael Kors, Boost Mobile, and United Health Group transform their media and measurement programs. The result? Profitable growth that speaks for itself.

At Ovative, we don't just track data, we redefine success. Our proprietary Mar Tech platform, EMRge helps businesses transform marketing into a driver of sustainable growth. Powered by Enterprise Marketing Return (EMR), our differentiated approach to holistic media buying, planning, and measurement, EMRge is the first Mar Tech platform to measure businesses holistically. We're all about raising the bar every day, and it shows.

Our work has been recognized by organizations like Digiday, Google, Inc. 5000, USA Today, and Search Engine Land.

In this role, you will be the technical owner of Ovative’s AI Analytics Platform, leading the design and delivery of a Retrieval-Augmented Generation (RAG) system and the supporting services required to make it secure, scalable, and usable across the business.

This role is intentionally hands-on and requires prior experience building RAG-style or AI-driven analytics systems. Ovative is early in this capability area, and this role is expected to bring proven architectural patterns and execution experience rather than learning them from scratch.

You will lead the build of platform components that do not exist today, including document ingestion and storage, indexing and vector search, semantic modeling, and system integrations. Many of these capabilities will leverage Databricks-native services, while others will require thoughtful integration with enterprise systems such as Confluence, Slack, and SSO via Entra.

• Serve as the technical owner of Ovative’s RAG-based AI Analytics Platform, leading the system from initial design through production use.
• Partner closely with Product Management to translate ambiguous business questions into clear, feasible platform capabilities, framing options and tradeoffs (accuracy, latency, security, and cost) with clear connection to business outcomes.
• Design and build new platform components, including:
  • Document ingestion and storage services
  • Indexing and vector database capabilities
  • Semantic layer abstractions for governed analytics
  • Orchestration logic coordinating retrieval, SQL generation, and model interaction
• Lead integrations with enterprise systems including:
• Authentication and authorization (SSO, entitlements)
• Knowledge sources (e.g., Confluence, internal file stores)
• User interfaces and request channels (e.g., Slack, EMRge, BI tools)
• Apply established RAG and AI analytics patterns to prevent hallucinations, enforce business logic, and ensure traceability of answers.
• Leverage and extend native solutions where appropriate, while defining clear system boundaries.
• Follow technical standards for AI safety, access control, observability, and cost management.
• Translate business questions into platform capabilities by partnering with Product and domain stakeholders.
• Break down complex platform initiatives into sequenced work that a small engineering team can execute effectively.
• Mentor other engineers by transferring context and patterns as the platform matures.

• 8+ years of experience in software engineering, data engineering, or applied analytics roles.
• Demonstrated experience designing and delivering RAG-based systems, search platforms, or AI-powered analytics applications in production.
• Experience working with embeddings, vector databases, and retrieval pipelines.
• Strong proficiency in Python and SQL, with experience building production data or platform services.
• Experience integrating multiple systems and tools (data platforms, document systems, authentication, user interfaces).
• Experience utilizing large complex datasets in advanced AI models to surface insights and recommendations.
• Familiarity with cloud data platforms (AWS, GCP, or Azure); experience with Databricks is strongly…