Engineer II – Machine Learning

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

Job Summary

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.

Essential Duties And 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

MANAGEMENT & SUPERVISORY RESPONSIBILTIES

• Direct supervisor job title(s) typically include: VP, Marketing Analytics
• Job may require managing Analytics associates

JOB QUALIFICATIONS: Essential Skills, Abilities, and Example Behavior(s)

DELIVER QUALITY RESULTS: Able to deliver top quality service to all customers (internal and external); Able to ensure all details are covered and adhere to company policies; Able to strive to do things right the first time; Able to meet agreed-upon commitments or advises customer when deadlines are jeopardized; Able to define high standards for quality and evaluate products, services, and own performance against those standards

TAKE INITIATIVE: Able to exhibit tendencies to be self-starting and not wait for signals; Able to be proactive and demonstrate readiness and ability to initiate action; Able to take action beyond what is required and volunteers to take on new assignments; Able to complete assignments independently without constant supervision

BE INNOVATIVE / CREATIVE: Able to examine the status quo and consistently look for better ways of doing things; Able to recommend changes based on analyzed needs; Able to develop proper solutions and identify opportunities

BE PROFESSIONAL: Able to project a positive, professional image with both internal and external business contacts; Able to create a positive first impression; Able to gain respect and trust of others through personal image and demeanor

ADVANCED COMPUTER USER: Able to use required software applications to produce correspondence, reports, presentations, electronic communication, and complex spreadsheets including formulas and macros and/or databases. Able to operate general office equipment including company telephone system

JOB QUALIFICATIONS: Education & Experience 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

Preferred Qualifications

• 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)