Overview
On Site
Depends on Experience
Accepts corp to corp applications
Contract - W2
Contract - Independent
Contract - 12 Month(s)
Skills
Artificial Intelligence
Cloud Computing
Computer Science
Conflict Resolution
Continuous Delivery
Continuous Integration
Customer Engagement
Data Engineering
Databricks
Machine Learning (ML)
Machine Learning Operations (ML Ops)
Management
Offshoring
Orchestration
Problem Solving
PyTorch
Python
Reporting
Technical Support
TensorFlow
Workflow
scikit-learn
Job Details
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.
- Bachelor s or Master s 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.
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