Lead Machine Learning Architect for Scalable Digital Platforms
5 days ago
**Job Title:** Lead Machine Learning Architect for Scalable Digital Platforms
At SourceBae, we are seeking a highly skilled and experienced Senior Machine Learning Engineer to lead the deployment and maintenance of ML pipelines in production. This is an excellent opportunity to work with cutting-edge technologies and make a significant impact on our digital platforms.
**Estimated Salary Range:** $150,000 - $200,000 per year
The ideal candidate will have hands-on expertise in Python, Docker, and cloud platforms (AWS preferred), with a strong foundation in ML best practices. They will be responsible for building and maintaining end-to-end feature engineering pipelines, developing and deploying scalable ML algorithms for batch and real-time processing, collaborating with data scientists and engineers to implement new features for digital platforms, ensuring quality standards with automated testing, CI/CD, and performance monitoring.
**Key Responsibilities:**
- Build and maintain end-to-end feature engineering pipelines
- Develop and deploy scalable ML algorithms for batch and real-time processing
- Collaborate with data scientists and engineers to implement new features for digital platforms
- Ensure quality standards with automated testing, CI/CD, and performance monitoring
**Required Skills and Qualifications:**
- Bachelor's degree in a STEM field; Master's preferred
- Experience in Python, Docker, and cloud platforms (AWS, GCP, Azure)
- Familiarity with CI/CD tools and infrastructure-as-code (Terraform, Ansible)
- Strong problem-solving and communication skills
**Benefits:**
- Opportunity to work with cutting-edge technologies
- Chance to make a significant impact on our digital platforms
- Competitive salary range ($150,000 - $200,000 per year)
**Others:**
- Review deployments, source codes, configurations, algorithms, models, repositories for completeness
- Identify missing libraries/codes, dependencies, code documentation
- Experience in establishing/scaling up data science functions
- Proven ability to discover solutions hidden in large datasets and drive business results with data-based insights
- Update technical documentation, code comments, etc
- Integrate the code into DevOps
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