Senior Lead Solution & Implementation Engineer - ML

Job 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.

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.

Hybrid remote