Company Background

The customer offers access solutions and products around the world. Their innovative offerings and partnerships, supported by a smart ecosystem, empower their clients with cutting-edge access solutions, ensuring safe traversal across homes, garages, community spaces, commercial areas, and storage units.

Project Description

The customer's products have now been integrated into devices such as garage door openers and lights, with the ability to be operated via a Wi-Fi network. These smart solutions are also being planned for inclusion in a widening range of upcoming home appliances. With the use of smartphones and internet-enabled devices, users can conveniently engage with these products online.

Technologies

Python
SQL
Apache Spark and/or Databricks
MLOps
MLFlow
Azure

What You'll Do

Deploy and maintain various types of AI/ML models in production including model performance tracking and CI/CD pipelines for model retraining;
Design, develop, and maintain a Feature Store, online and offline, as needed to accelerate data science initiatives and support real-time use cases;
Design, develop, and maintain online and offline model serving;
Develop and implement standards and best practices for MLOps discipline and ensure consistency to these standards;
Work closely with data scientists, software engineers, and data engineers to ensure successful deployment and management of AI/ML models;
Develop and maintain MLOps related infrastructure;
Stay abreast of current and emerging technologies to improve efficiency, reliability, and scalability, including emerging machine learning, data management, and AI capabilities with the real-time implementation of AI/ML software;
Partner with the Lead Architect on ML architecture and solution design;
Perform design, code, and test plan reviews in support of maintaining data engineering standards;
Implement a modern Model Lifecycle, in conjunction with Data Scientists and Researchers, to drive a rapid, iterative lifecycle from request to delivery of models;
Develop and implement an efficient and scalable framework to take models from data scientists to fully optimized and implemented production processes;
Collaborate with data scientists to prepare data for model development and production;

Job Requirements

Bachelor’s degree in computer science or related quantitative field of study or related experience;
5+ years of experience in data science or engineering roles, including hands-on experience designing and developing machine learning solution;
3+ years of hands-on professional MLOps engineering experience including understanding of container technology, Restful API and other various model deployment techniques and model performance measurement in production;
Knowledge of modern machine learning techniques;
Proficiency in programming and querying languages, including Python and SQL;
Proficiency in big data processing, including Apache Spark and/or Databricks;
Proficiency in technologies like MLFlow, Azure ML services, AutoML;
Track record of leading projects and cross-functional teams;
Problem-solving and analytical thinking skills;
Experience working with Agile methodologies;
Experience with IOT, software-defined customer experiences, private equity backed companies;
Level of English - from Intermediate (spoken/written);

What Do We Offer

The global benefits package includes:

Technical and non-technical training for professional and personal growth;
Internal conferences and meetups to learn from industry experts;
Support and mentorship from an experienced employee to help you professional grow and development;
Internal startup incubator;
Health insurance;
English courses;
Sports activities to promote a healthy lifestyle;
Flexible work options, including remote and hybrid opportunities;
Referral program for bringing in new talent;
Work anniversary program and additional vacation days.

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MLOps Engineer (IoT sphere)