What are the responsibilities and job description for the Machine Learning Engineer position at Zensark Inc?
Job Details
Machine Learning Engineer :
AWS Expertise
- Deep knowledge of the AWS stack, especially AWS SageMaker, AWS Glue, and Bedrock.
- AWS certification is preferred.
Machine Learning Development
- Lead development of ML-integrated software algorithms in production.
- Proficient in advanced ML techniques, including Natural Language Processing (NLP) and Natural Language Understanding (NLU).
- Expertise in ML model development, deployment architecture, and end-to-end automation.
Data Pipeline Design
- Lead and optimize complex model development and deployment pipelines for batch and real-time environments.
- Design innovative and efficient data pipelines using automation technologies.
- Diagnose and resolve data inconsistencies, ensuring seamless integration and optimization.
Data Discovery and Preparation
- Collaborate with data science teams to understand data requirements.
- Analyze raw data for quality and align it with business and model development needs.
- Innovate data discovery techniques and apply business context to data analysis.
Stakeholder Engagement
- Engage with stakeholders to understand business processes and translate them into analytic approaches.
- Influence business planning and departmental priorities with structured problem-solving.
Model Monitoring and Maintenance
- Write and execute model monitoring scripts.
- Diagnose and resolve issues based on monitoring alerts.
- Plan and coordinate responses to ensure operational stability.
Strategic Contributions
- Serve as a thought leader and SME for machine learning engineering.
- Contribute to enterprise-wide strategic initiatives and growth of the analytics community.
Technical Expertise and Leadership
- Expert knowledge of MDLC (Machine Learning Development Lifecycle) tools and processes.
- Apply methodologies from statistics, optimization, probability theory, and experimental design to AI system automation.
- Provide technical guidance to cross-functional teams and Machine Learning Operations teams.
This role demands strong technical acumen, innovative thinking, and the ability to bridge the gap between data science and engineering in production environments
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