ML Data Engineer: Feature Pipelines & MLOps

PODS; Clearwater (FL); On-site; USD 120,000 - 160,000; Full time

Job summary

PODS is seeking a Data Engineer- Machine Learning to scale a modern data & AI stack and drive business impact. You will bridge data engineering and ML engineering, building feature pipelines in Snowflake and Databricks, and productionizing batch/real-time inference with MLOps practices.

You will collaborate with the ED&A team to enrich data into governed models/feature tables and optimize models to grow revenue and CX, while ensuring governance and cost efficiency across platforms.

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 & ML
• Partner with Data Science to optimize models that grow customer base and revenue
• Implement MLOps/LLMOps: experiment tracking, reproducible training, model/asset registry, safe rollout, retraining triggers
• Enforce data governance & security policies and contribute metadata, lineage, and definitions to the ED&A catalog
• Optimize cost/performance across Snowflake/Snowpark and Databricks
• Follow robust version control and DevOps practices
• Create runbooks and documentation for analytics, data engineering, and product partners

Job description

PODS is seeking a Data Engineer- Machine Learning to scale a modern data & AI stack and drive business impact. You will bridge data engineering and ML engineering, building feature pipelines in Snowflake and Databricks, and productionizing batch/real-time inference with MLOps practices.

You will collaborate with the ED&A team to enrich data into governed models/feature tables and optimize models to grow revenue and CX, while ensuring governance and cost efficiency across platforms.