Engineer II - Machine Learning

PODS•Clearwater, FL

23h

About The Position

The Data Engineer- Machine Learning is responsible for scaling a modern data & AI stack to drive revenue growth, improve customer satisfaction, and optimize resource utilization. As an ML Data Engineer, you will bridge data engineering and ML engineering: build high‑quality feature pipelines in Snowflake/Snowpark, Databricks, productionize and operate batch/real‑time inference, and establish MLOps/LLMOps practices so models deliver measurable business impact at scale.

Requirements

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

Nice To Haves

Familiarity with LLMOps patterns for generative AI applications
Experience with NLP, call center data, and voice analytics
Exposure to feature stores, model registries, canary/shadow deploys, and A/B testing frameworks
Marketing analytics domain familiarity (lead scoring, propensity, LTV, routing/prioritization)

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 our Enterprise Data & Analytics (ED&A) team centered on replicating operational data into Snowflake, enriching it into governed, reusable models/feature tables, and enabling advanced analytics & ML—with Databricks as a core collaboration environment
Partner with Data Science to optimize models that grow customer base and revenue, improve CX, and optimize resources
Implement MLOps/LLMOps: experiment tracking, reproducible training, model/asset registry, safe rollout, and automated 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 and established version control and DevOps practices
Create clear runbooks and documentation, and share best practices with analytics, data engineering, and product partners