AI Engineer: Generative AI & MLOps on Lakehouse

Perficient; United States; On-site; USD 150,000 - 230,000; Full time

Job summary

Perficient seeks a Databricks AI Engineer to design and deploy scalable AI solutions on the Databricks Lakehouse Platform. You will build data pipelines with Spark, Delta Lake, and Unity Catalog, and develop end-to-end ML workflows using MLflow and Databricks ML.

The role emphasizes Generative AI, LLMs, RAG-based apps, and strong MLOps practices across CI/CD, governance, and monitoring to ensure enterprise-grade performance and cost efficiency.

Qualifications

• 5+ years of experience in AI/ML, Data Engineering, or Software Engineering.
• 3+ years of hands-on experience with Databricks and Apache Spark.
• Strong proficiency in Python, PySpark, and SQL.
• Experience with MLflow, Delta Lake, Unity Catalog, and Databricks Workflows.
• Experience building and deploying Generative AI, LLM, and RAG-based applications.
• Knowledge of cloud platforms such as Azure, AWS, or GCP.
• Understanding of MLOps, model deployment, and AI governance best practices.

Responsibilities

• Design and implement AI/ML and Generative AI solutions using Databricks.
• Develop scalable data pipelines with Apache Spark, Delta Lake, and Unity Catalog.
• Build, train, deploy, and monitor ML models using MLflow and Databricks ML.
• Develop RAG-based applications, AI agents, and LLM-powered solutions.
• Implement MLOps best practices including CI/CD, model governance, and monitoring.
• Collaborate with business, data, and engineering teams to translate requirements into technical solutions.
• Optimize performance, scalability, security, and cost efficiency of AI platforms.

Skills

AI/ML

Python

PySpark

SQL

Cloud computing

MLOps

Generative AI

RAG

LLMs

Tools

Databricks

Apache Spark

MLflow

Delta Lake

Unity Catalog

LangChain

LangGraph

Azure

AWS

GCP

Job description

Perficient seeks a Databricks AI Engineer to design and deploy scalable AI solutions on the Databricks Lakehouse Platform. You will build data pipelines with Spark, Delta Lake, and Unity Catalog, and develop end-to-end ML workflows using MLflow and Databricks ML.

The role emphasizes Generative AI, LLMs, RAG-based apps, and strong MLOps practices across CI/CD, governance, and monitoring to ensure enterprise-grade performance and cost efficiency.