Job Title: Machine Learning Engineer
Location: Plano, TX – Onsite/Hybrid
Job Type: Contract
Work Authorization: STRICTLY ON W2
Job Summary
We are looking for a Machine Learning Engineer with a strong engineering and model-development mindset. The ideal candidate will have hands-on experience with Python, machine learning model development, deployment, troubleshooting, and production support across Windows and Linux environments.
This is not primarily a DevOps or SRE position. We are looking for someone who understands the engineering side of machine learning, including model development and lifecycle, while also being comfortable supporting ML applications in production.
Key Responsibilities
Develop, maintain, deploy, and troubleshoot machine learning applications and models.
Work closely with Data Scientists and ML Engineers to take models from development through production.
Develop and maintain Python-based ML applications and services.
Support ML applications running on Windows and Linux environments.
Manage and troubleshoot Kubernetes clusters and Docker containers supporting ML workloads.
Deploy and manage machine learning models throughout their lifecycle.
Debug complex production issues using Python, logs, monitoring, and troubleshooting tools.
Implement monitoring and alerting using Datadog to ensure application and model health.
Automate repetitive engineering and operational tasks using Python and other scripting technologies.
Work with CI/CD pipelines to support reliable ML application and model deployments.
Collaborate with engineering, data science, and infrastructure teams.
Ensure ML applications meet requirements for performance, security, scalability, and availability.
Document architecture, deployment processes, model workflows, and troubleshooting procedures.
Required Skills
Strong hands-on Python programming experience.
Strong understanding of Machine Learning concepts and workflows.
Experience with ML model development and/or model engineering.
Hands-on experience with ML model deployment and lifecycle management.
Experience supporting applications in both Windows and Linux environments.
Experience with on-premises servers and production environments.
Hands-on experience with Kubernetes and Docker.
Experience troubleshooting distributed applications and production issues.
Experience with Datadog or similar monitoring/observability tools.
Experience with CI/CD pipelines for ML applications.
Familiarity with AWS cloud services.
Understanding of DevOps/SRE practices as they relate to supporting ML applications.
Strong problem-solving and debugging skills.
Excellent communication and collaboration skills.
Ideal Candidate Profile
We are specifically looking for an engineering-oriented Machine Learning Engineer who can understand and contribute to model development, not just infrastructure or operations.
Strong candidates will have:
Machine Learning + Python development experience
ML model development/deployment experience
Production application troubleshooting experience
Kubernetes/Docker experience
Windows/Linux administration experience
Experience working closely with Data Scientists
The candidate should be stronger on ML engineering and application/model development than pure infrastructure or operations.
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