MLOps Lead Engineer

Overview

On Site
Depends on Experience
Contract - W2
Contract - 12 Month(s)
No Travel Required

Skills

MLOpsLeadEngineer
Machine Learning (ML)
Machine Learning Operations (ML Ops)
CI/CD pipelines
AWS
Kubernetes
Docker
RBAC
TensorFlow
Data Science
Data Governance
Management
Cloud Computing
Collaboration
Workflow
Python
Terraform
Edge deployment
MLOpsEngineer
data engineers
data scientists
ML pipelines
ensorFlow Serving
TorchServe
MLflow
Access Control
Privacy
Continuous Integration
MLOps & Cloud Deployment
Data Science & ML Engineering
LLM applications
Scikit-learn
Hugging Face
Machine Learning Research Assistant
Designed and implemented NLP pipelines
Developed and fine-tuned ML and NLP models
Designed and delivered end-to-end ML workflows on AWS SageMaker
Orchestrated ML pipelines
Established CI/CD
observability
and governance practices

Job Details

MLOps Lead Engineer
Need Local only : Dallas- Tx F2F Interview

Custom Skill Requirements

MLOps 14+ Years of Experience
CI/CD pipelines
AWS
Python
TensorFlow
Terraform Contractor Qualifying Questions
Are you local to Dallas?
Are you able to go to Dallas office 3days/wk?
Are you able to travel to a manufacturing plant?
Do you have MLOps Engineering experience?
Can you build MLOps pipeline?
Have you worked on Edge deployment? We are seeking an MLOps Engineer to bridge the gap between data science and production systems, ensuring that machine learning models are deployed, monitored, and maintained at scale. You will work closely with data scientists, data engineers, and software developers to design and implement automated, reliable, and secure ML pipelines from development to production.

Key Responsibilities

Model Deployment & Serving

Deploy ML models into production environments using tools such as Docker, Kubernetes, and model serving frameworks (e.g., TensorFlow Serving, TorchServe, MLflow).

Implement CI/CD pipelines for ML workflows.

Pipeline Development & Automation

Build and maintain end-to-end machine learning pipelines for data ingestion, preprocessing, training, validation, deployment, and monitoring.

Automate model retraining and versioning to ensure continuous improvement.

Monitoring & Maintenance

Set up monitoring and alerting systems for model performance, data drift, and infrastructure health.

Troubleshoot and resolve model degradation issues in production.

Collaboration & Integration

Collaborate with data scientists to transition models from experimentation to production-ready systems.

Work with DevOps and cloud teams to ensure ML workloads are scalable and cost-efficient.

Security & Compliance

Ensure compliance with data governance, security, and privacy regulations.

Manage role-based access control (RBAC) for ML infrastructure.
MLOps pipeline automation

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