ML Data Engineer: Scalable Pipelines & MLOps

PODS; Clearwater (FL); On-site; USD 110,000 - 170,000; Full time

Job summary

PODS is seeking an experienced Data Engineer- Machine Learning to scale a modern data & AI stack in Clearwater, FL. The role bridges data engineering and ML engineering to build feature pipelines in Snowflake and Databricks, productionize inference, and implement MLOps/LLMOps for scalable business impact.

The ideal candidate delivers batch and real-time pipelines, collaborates with the ED&A team, and ensures governance, observability, and cost optimization across platforms.

Qualifications

• Bachelor's or Master's in CS, Data/ML, or related field (or equivalent experience).
• 4 years in data/ML engineering building production-grade pipelines with Python and SQL.
• Strong hands-on with Snowflake/Snowpark and Databricks; comfort with Tasks & Streams for orchestration.
• 2 years of experience optimizing models: batch jobs and/or real-time APIs, containerized services, CI/CD, and monitoring.
• Solid understanding of data modeling and governance/lineage practices expected by ED&A.

Responsibilities

• Design, build, and operate feature pipelines that transform curated datasets into reusable, governed feature tables in Snowflake.
• Productionize ML models (batch and real-time) with reliable inference jobs/APIs, SLAs, and observability.
• Setup processes in Databricks and Snowflake/Snowpark to schedule, monitor, and auto-heal training/inference pipelines.
• Collaborate with ED&A to replicate operational data into Snowflake and enable advanced analytics with Databricks.
• Partner with Data Science to optimize models that grow customer base and revenue while improving CX.

Skills

Python

SQL

Snowflake/Snowpark

Databricks

MLOps/LLMOps

Data engineering

Feature engineering

Education

Bachelor's or Master's in CS/Data/ML or related field

Tools

Snowflake/Snowpark

Databricks

Job description

PODS is seeking an experienced Data Engineer- Machine Learning to scale a modern data & AI stack in Clearwater, FL. The role bridges data engineering and ML engineering to build feature pipelines in Snowflake and Databricks, productionize inference, and implement MLOps/LLMOps for scalable business impact.

The ideal candidate delivers batch and real-time pipelines, collaborates with the ED&A team, and ensures governance, observability, and cost optimization across platforms.