W2 Role :: Lead MLOps :: Columbus, Ohio (Onsite)

Overview

On Site
Full Time
Accepts corp to corp applications
Contract - W2
Contract - Independent
Contract - 12 Month

Skills

Machine Learning Operations (ML Ops)
Amazon SageMaker
Training
ISO 9000
Testing
IT Management
Onboarding
Machine Learning (ML)
Orchestration
Kubernetes
Docker
Python
Software Engineering
Continuous Integration
Continuous Delivery
Cloud Computing
Regulatory Compliance
Open Source
Artificial Intelligence
Generative Artificial Intelligence (AI)
LinkedIn
Oracle UCM

Job Details

Job Title Lead MLOps

Location Columbus, Ohio (Onsite)

Duration 12 Month Contract to hire role

Proper LinkedIn
W2 Role

Job Description

  • Has to have Sagemaker or MLflow/metaflow or something similar
  • Kubernetes is a must

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.

Akshit Sisonia - Sr. Technical Recruiter

Email -

LinkedIn -

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