Senior Lead Solution & Implementation Engineer - ML

Description

We are looking for a skilled Senior Lead Solution & Implementation Engineer - Machine Learning Platform (Open Source Stack) to join our team on a contract basis in Columbus, Ohio. This role involves designing and implementing scalable infrastructure to support machine learning models in production environments. You will collaborate with cross-functional teams to ensure compliance, optimize workflows, and enhance the deployment process.

Key Responsibilities

• Develop and maintain secure, scalable infrastructure for ML model training, testing, and deployment using open-source tools.
• Create reusable deployment templates to standardize production workflows across teams.
• Translate prototype models into resilient, monitored, and observable production systems.
• Implement guardrails and controls to ensure compliance with internal standards (e.g., SR 11-7, ISO 42001).
• Collaborate with data scientists to streamline onboarding to platform capabilities.
• Establish CI/CD pipelines with integrated testing, scanning, and validation of model code and artifacts.
• Lead cross-functional delivery efforts involving model onboarding and platform integration.

Requirements

Required Qualifications

• 6+ years of experience in software, data, or ML engineering roles.
• Hands-on experience with ML orchestration tools such as MLflow, Metaflow, Airflow, or similar.
• Production experience with Kubernetes, Docker, and Helm.
• Strong proficiency in Python and software engineering best practices.
• Experience implementing CI/CD pipelines and infrastructure-as-code in cloud or hybrid environments.

Preferred Qualifications

• Experience in regulated industries or environments with strong risk and compliance requirements.
• Familiarity with open-source tools for model monitoring, drift detection, or lineage (e.g., Evidently AI, Feast, LakeFS).
• Experience serving models using KServe, Ray Serve, or Triton Inference Server.
• Knowledge of 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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