AI Data & Tools Engineer

We're seeking a multifaceted AI Data and Tools Engineer to rapidly develop and deploy intelligent tools that leverage big data infrastructure and modern AI architectures. This role combines strong software engineering fundamentals with the ability to build production-ready AI applications at speed, including integration with Model Context Protocol (MCP) systems.

Description

The AI Data & Tools Engineer will play a critical role in building the next generation of intelligent data tools and AI-powered applications. You'll work at the intersection of large-scale data infrastructure, modern AI/ML systems, and user-facing applications to create tools that accelerate productivity and enable data-driven decision making across the organization.

In this role, you'll architect and implement end-to-end solutions that process massive datasets, integrate with modern AI models and frameworks, and deliver intuitive interfaces for both technical and non-technical users. You'll have the opportunity to work with the latest AI technologies including LLMs, vector databases, and AI agent architectures, while employing enterprise-scale data platforms.

This position requires a unique blend of skills: the engineering rigor to build scalable, production-grade systems; the AI/ML expertise to efficiently harness modern models and frameworks; and the product sensibility to create tools that people actually want to use. You'll be empowered to move quickly, make pragmatic technical decisions, and ship impactful solutions iteratively.

","responsibilities":"Design, develop, and deploy production-ready AI-powered tools and applications that tackle real business problems

Build and integrate Model Context Protocol (MCP) servers to enable AI assistants and agent-based workflows

Implement AI agent architectures with tool-calling capabilities for autonomous task execution

Create intelligent data processing pipelines that leverage LLMs, embeddings, and retrieval systems

Architect and implement scalable data pipelines using big data technologies (Spark, Kafka, streaming platforms)

Build integrations with cloud data warehouses (Snowflake, BigQuery, Redshift) and data lakes

Build responsive web applications and user interfaces using modern frameworks (React, Vue, Angular)