Overview
On Site
Accepts corp to corp applications
Contract - W2
Contract - Independent
Contract - 6+ month(s)
Skills
Artificial Intelligence
Offshoring
Technical Support
Collaboration
Computer Science
Customer Engagement
Reporting
Python
TensorFlow
PyTorch
scikit-learn
Databricks
Management
Machine Learning Operations (ML Ops)
Cloud Computing
Continuous Integration
Continuous Delivery
Workflow
Conflict Resolution
Problem Solving
Machine Learning (ML)
Data Engineering
Orchestration
Job Details
Job Title: AI / MLOps Lead
Location: Austin, TX - Day 1 Onsite only
Contract
Job Description
We are seeking a Tech Lead for a support related role for deployed ML solutions. You ll act as a bridge between the client and the offshore team ensuring timely resolution of support incidents, implementation and deployment of change requests as well as finding avenues for automation in order to reduce effort.
Responsibilities
- Technical Support: Provide technical support for production machine learning models, including troubleshooting and resolving ML pipeline issues, model performance degradation, data anomalies, and deployment failures.
- Shift: You should be willing to work on a shift based schedule and should be ready to provide on call weekend support
- Collaboration: Work closely with internal and client side stakeholders to address problems and implement best practices.
Qualifications
- Bachelors or Masters degree in Computer Science, Engineering or related field
- 8+ years of experience in machine learning engineering, including deploying, maintaining, and troubleshooting ML models in production.
- Experience in leading a team, client interaction and reporting.
- Strong hands on experience with Python and ML frameworks (TensorFlow, PyTorch, Scikit learn, etc.).
- Strong hands on experience with Databricks for building, deploying, and managing machine learning workflows.
- Hands on with MLOps tools and cloud platforms
- Understanding of CI/CD for ML workflows.
- Proven problem solving skills, especially in diagnosing and resolving issues in large scale, distributed ML systems.
- Strong understanding of data engineering concepts, pipeline orchestration, and model versioning.
- Knowledge of model
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