Engineer II - Machine Learning

About the role

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. You will bridge data engineering and ML engineering by building high-quality feature pipelines, productionizing and operating batch/real-time inference, and establishing MLOps/LLMOps practices so models deliver measurable business impact at scale.

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 Enterprise Data & Analytics to replicate operational data into Snowflake, enrich it into governed, reusable models/feature tables, and enable 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

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

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)