Remote - Principal AWS AI/ML Data Engineer

 Job Title: Principal AWS AI/ML Data Engineer

Location: 100% Remote role

Duration: 12 Plus Months 

Job Description:

eDataForce has teamed up with a growing leader in health technology firm that just went through an IPO. They are looking for a hands-on Principal AWS AI/ML Data Engineer with a passion for working on deep complex problems. This is a unique opportunity to be one of the early members and a key contributor of the new, top-notch Data team that is growing. You bring data engineering best practices to production and maintenance of ML and analytics code.

You will be responsible for:

• Designing and implementing new cloud-based data and ML solutions – new data processing, datasets, and systems to support data science and advanced analytics needs.
• Work closely with Product, Data Science, Engineering, and subject matter experts to build the end-to-end AI/ML solutions meeting the business objectives and customers’ needs with rapid progress, agile development.
• Build machine learning Ops frameworks to support data science and advanced analytics using AWS cloud services and Snowflake DWH.
• Build feedback loops to track AI/ML and Data product/feature usage, quality, and impact on end users, continuously improve to provide the best customer experience.
• Build Orchestrator to allow data science, engineering, and product experiment, tuning features, or try out new ideas.
• Develop and maintain a set of frameworks, APIs and tools on top of several libraries for faster ML research to production lifecycle.
• Develop scalable tools and services for handling machine learning training and predictions.
• Identify emerging ML/AI algorithms applicable to marketing science problems and adapt and develop proof of concepts on real world data.

Requirements:

• Graduate degree in Computer Science or other quantitative fields such as Engineering, Statistics, Mathematics, Machine Learning, Decision Science, Data Analytics, etc. Master and
• 10+ years of professional experience, or Ph.D. and 6+ years of professional experience.
• 10+ years of experience programming in Java/Scala/C# and/or Python, with understanding of distributed computing.
• 6+ years of experience in software engineering, including 4+ years in Data and AI/ML engineering, AI platform, AI/ML products.
• 3+ years’ experience with Kubernetes, Docker, deploying containers, automated build deployments or AWS Compute & Container Services.
• Solid experience and understanding of architecting, designing, and operationalization of large-scale data and analytics solutions on Cloud Data Warehouse such as Snowflake or Google BigQuery is a must.
• Expertise in using AWS cloud-based systems and services to acquire and deliver data.
• Excellent SQL knowledge and hands-on experience with the ability to create efficient data models.
• Extensive experience and working knowledge in data ETL, building AI/Data platform and ML solutions, Deep Learning, Knowledge Graph, NLP, classification, and optimization; Graph Neural Networks skills is a plus.