ML/AI Engineer - Databricks Agentic AI

Harnham; États-Unis; Sur place; USD 120 000 - 180 000; Plein temps

Résumé du poste

Harnham seeks a highly skilled AI/ML Platform Engineer to join a large enterprise building a multi-agent analytics platform that enables users to query data through natural language and receive trustworthy, explainable answers at scale.

You will design and operate a system of cooperating LLM-powered agents that query data sources, run analyses, generate visualizations, and deliver production-grade insights. Collaboration with engineering and product teams is essential to success.

Qualifications

• Deep expertise with Databricks platform and production AI systems.
• Experience designing and operating multi-agent AI workflows.

Responsabilités

• Design and implement multi-agent workflows within a Databricks-based analytics platform, including planning, routing, tool calling, memory, and handoff between specialized agents.
• Build and maintain agent tools including data source connectors, retrieval pipelines, SQL generation and validation, visualizations, and result verification.
• Develop evaluation frameworks, testing suites, and regression tracking to improve quality and reliability.
• Improve answer accuracy and groundedness through guardrails, validation mechanisms, and explainability.
• Optimize for latency, cost, throughput, and production resilience.
• Partner with engineering, analytics, and product teams to deliver trusted AI-powered analytics experiences.

Connaissances

Databricks platform

Python engineering

LLM / agentic apps

OpenAI Agents SDK

Multi-agent architectures

Tool calling

RAG

CI/CD pipelines

Cloud-native deployments

Outils

Databricks Apps

Databricks SDK

Databricks Asset Bundles

Databricks APIs

LangGraph

OpenAI Agents SDK

Vector Search

MLflow

CI/CD

Cloud-native deployments

Description du poste

This is an opportunity to join a large enterprise organization building a multi-agent analytics platform that enables users to query data through natural language and receive trustworthy, explainable answers. You will help design and operate a system of cooperating LLM-powered agents that query data sources, run analysis, generate visualizations, and deliver production-grade insights at scale.

The Company

They are a large media and entertainment organization investing heavily in AI and analytics capabilities. As part of a broader transformation initiative, they are building intelligent systems that enable users to interact with complex data through natural language while maintaining enterprise-grade reliability, governance, and performance. This role sits within a highly technical team focused on delivering scalable, production-ready AI applications.

The Role and Deliverables

• Design and implement multi-agent workflows within a Databricks-based analytics platform, including planning, routing, tool calling, memory, and handoff between specialized agents.
• Build and maintain agent tools including data source connectors, retrieval pipelines, SQL generation and validation, visualizations, and result verification.
• Develop evaluation frameworks, testing suites, and regression tracking to improve quality and reliability.
• Improve answer accuracy and groundedness through guardrails, validation mechanisms, and explainability.
• Optimize for latency, cost, throughput, and production resilience.
• Partner with engineering, analytics, and product teams to deliver trusted AI-powered analytics experiences.

Your Skills & Experience

• Deep understanding of the Databricks platform, including hands-on experience with Databricks Apps, Databricks SDK, Databricks Asset Bundles, and Databricks APIs.
• Experience with Mosaic AI, Model Serving, Vector Search, MLflow, Unity Catalog, or similar Databricks-native capabilities.
• Strong experience building production AI, LLM, or agentic applications.
• Strong Python engineering skills and experience with frameworks such as LangGraph, OpenAI Agents SDK, or similar.
• Hands-on experience with tool calling, RAG, prompt engineering, and multi-agent architectures.
• Experience with agent evaluation frameworks, observability tooling, and production AI operations.
• Experience building and operating production services, APIs, CI/CD pipelines, and cloud-native applications.