Lead ML Data Engineer

  • Columbus, OH
  • Posted 23 hours ago | Updated 23 hours ago

Overview

On Site
$60 - $65
Contract - W2
Contract - 3 Month(s)
No Travel Required

Skills

kubernetes
data engineering
machine learning
ai
open source models
mlflow
metaflow
python
sagemaker

Job Details

Our national banking client is seeking a Lead ML Data Engineer to join their team!

3-month contract to hire

Columbus, OH - 4 days onsite, 1 day remote

Must be authorized to work in the US w/o sponsorship

Responsibilities

  • Build and maintain secure, scalable infrastructure for ML model training, testing, and deployment using open-source tools.
  • Create reusable deployment templates that standardize the path to production across teams.
  • Translate prototype models into resilient, monitored, and observable production systems.
  • Implement guardrails and controls that ensure compliance with internal standards (e.g., SR 11-7, ISO 42001).
  • Partner with data scientists to simplify onboarding to platform capabilities.
  • Establish CI/CD pipelines with hooks for testing, scanning, and validation of model code and artifacts.
  • Serve as a technical lead for cross-functional delivery efforts involving model onboarding and platform integration.
Required Qualifications
  • 6+ years of experience in software, data, or ML engineering roles.
  • Strong hands-on experience with tools like MLflow, Metaflow, Airflow, or similar orchestration frameworks.
  • Production experience with Kubernetes, Docker, and Helm.
  • Deep understanding of Python and software engineering best practices.
  • Experience implementing CI/CD pipelines and infrastructure-as-code in a cloud or hybrid environment.
Preferred Qualifications
  • Experience working in regulated industries or environments with strong risk and compliance expectations.
  • Familiarity with open-source model monitoring, drift detection, or lineage tools (e.g., Evidently AI, Feast, LakeFS).
  • Hands-on experience serving models using KServe, Ray Serve, or Triton Inference Server.
  • Familiarity with enterprise security tools like Trivy, Aqua, or Snyk for code and container scanning.
  • Exposure to LLM/RAG architecture or GenAI platform integration.

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