Machine Learning Engineer
Tiger AnalyticsJob Description
Tiger Analytics is looking for experienced Machine Learning Engineers with Gen AI experience to join our fast-growing advanced analytics consulting firm. Our employees bring deep expertise in Machine Learning, Data Science, and AI. We are the trusted analytics partner for multiple Fortune 500 companies, enabling them to generate business value from data. Our business value and leadership has been recognized by various market research firms, including Forrester and Gartner.
We are looking for top-notch talent as we continue to build the best global analytics consulting team in the world. You will be responsible for:
- Providing solutions for the deployment, execution, validation, monitoring, and improvement of MLE solutions
- Creating Scalable Machine Learning systems .
- Building reusable production data pipelines for implemented machine learning models
- Writing production-quality code and libraries that can be packaged as containers, installed and deployed
You will collaborate with cross-functional teams and business partners and will have the opportunity to drive current and future strategy by leveraging your analytical skills as you ensure business value and communicate the results.
Requirements
MLE Skillsets needed:
• Good exposure to Model development and deployment
• Experience developing API’s
• Python based application using CICD
• AWS or Google cloud environment experience
Required Skills:
• Proficiency in SQL, Python, and MLOps.
• Experience with AWS services, including SageMaker
• Familiarity with DevOps concepts and cloud based CI/CD tools
• Knowledge of Snowflake and Oracle databases.
• Experience with GitHub for version control.
• Strong understanding of Agile methodologies.
Preferred Qualifications:
• Passion for learning and staying updated with new technologies.
• Persistent and proactive approach to problem-solving.
• Strong analytical mindset.
• Knowledge of the insurance industry is a plus.
• Exposure to GenAI and LLMOps(Large Language Model Operations) a differentiator, but not required.
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