Senior Data & AI Engineer – Databricks & Mosaic AI

1000 Accenture LLP Company

Dallas (TX)

Hybrid

USD 90,000 - 150,000

Full time

4 days ago

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Benefits offered by this job

Medical benefits

401(k) plan

Bonus opportunities

Job summary

Accenture’s Advanced Technology Centers in Dallas is seeking an experienced Data & AI Engineer to design, build, and optimize scalable data pipelines using Azure Databricks. You will implement autonomous agents with Mosaic AI and frameworks like LangChain or AutoGen, collaborating with analytics, data science, and cloud teams to ensure reliable data delivery.

The role requires at least three years in Data & AI and Azure cloud services, plus two years with Databricks (PySpark, SQL, Delta Lake,

Qualifications

Three+ years of professional experience as a Data & AI Engineer.
Three+ years of experience with Microsoft Azure cloud data services.
Two+ years of experience with Databricks (PySpark, SQL, Delta Lake, workflows).
Bachelor's degree or equivalent (minimum 12 years) work experience.
If Associate Degree, must have minimum 6 years work experience.

Responsibilities

Architect, develop, and maintain scalable, low-latency data pipelines to ingest and transform diverse datasets leveraging Azure Databricks workflows and Spark technologies.
Design and optimize Lakehouse architecture using Delta Lake tables following Bronze, Silver, and Gold layers to ensure efficient, reliable, and governed data storage.
Partner closely with data scientists and business analysts to deliver clean, validated, and well-modeled datasets that support advanced analytics and AI initiatives.
Design and implement autonomous agents using Databricks Mosaic AI Agent Framework and frameworks like LangChain or AutoGen.
Build and optimize vector search indexes using Databricks Vector Search for accurate context retrieval.
Process large-scale distributed datasets on the Databricks Intelligence Platform with Apache Spark.
Design and implement data models that meet business requirements and enhance data usability.
Continuously evaluate and improve data architecture processes to enhance efficiency and effectiveness.
Design AI agents with retrieval, orchestration, tool invocation, evaluation harnesses, and lifecycle observability.
Continuously monitor pipeline health and performance; implement automated alerting and observability frameworks to facilitate rapid troubleshooting and proactive optimization.