Experienced MLOps Engineer
3 weeks ago
Moative, an Applied AI company, designs and builds transformation AI solutions for traditional industries in energy, utilities, healthcare & life sciences, and more. Through Moative Labs, we build AI micro-products and launch AI startups with partners in vertical markets that align with our theses.
Our Past: We have built and sold two companies, one of which was an AI company. Our founders and leaders are Math PhDs, Ivy League University Alumni, Ex-Googlers, and successful entrepreneurs.
Our Team: Our team of 20+ employees consist of data scientists, AI/ML Engineers, and mathematicians from top engineering and research institutes such as IITs, CERN, IISc, UZH, Ph.Ds. Our team includes academicians, IBM Research Fellows, and former founders.
Role Description
As an ML Ops specialist, you will work on data architecture, large-scale processing systems, and data flow management. You will build and maintain optimal data architecture and data pipelines, assemble large, complex data sets, and ensure that data is readily available to data scientists, analysts, and other users. In close collaboration with ML engineers, data scientists, and domain experts, you'll deliver robust, production-grade solutions that directly impact business outcomes. Ultimately, you will be responsible for developing and implementing systems that optimize the organization's data use and data quality.
Qualifications
- 3 - 5 years of hands-on experience as a data engineer, data architect or similar role, with a good understanding of data structures and data engineering.
- Solid knowledge of cloud infra and data-related services on AWS (EC2, EMR, RDS, Redshift) and/ or Azure.
- Advanced knowledge of SQL, including writing complex queries, stored procedures, views, etc.
- Strong experience with data pipeline and workflow management tools (such as Luigi, Airflow).
- Experience with common relational SQL, NoSQL and Graph databases.
- Strong experience with scripting languages: Python, PySpark, Scala, etc.
- Practical experience with basic DevOps concepts: CI/CD, containerization (Docker, Kubernetes), etc
- Experience with big data tools (Spark, Kafka, etc) and stream processing.
- Excellent communication skills to collaborate with colleagues from both technical and business backgrounds, discuss and convey ideas and findings effectively.
- Ability to analyze complex problems, think critically for troubleshooting and develop robust data solutions.
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- Ability to identify and tackle issues efficiently and proactively, conduct thorough research and collaborate to find long-term, scalable solutions.
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