ML/DATA OPS ENGINEER

Feelit is an Industry 4.0 company operating in the field of Predictive Maintenance, which is integral for sustainable smart manufacturing, better supporting value creation. Feelit automatically alerts of upcoming failures in real-time, preventing unscheduled downtime, and contributing to the efficient use of resources by ensuring optimum system availability.

Feelit is looking for a passionate ML/Data Ops Engineer to join the Data Science and Analytics team and take part of the amazing fast growth of the field. The main and very important position includes extending the company ML and Data architecture and supporting the fast scale up of the company towards the extensive cloud and edge analytics solution.

Responsibilities:

·      Taking part in the development of cutting-edge predictive maintenance models by leveraging various Machine Learning approaches.

·      Be responsible for the entire algorithmic life-cycle - from models training, evaluation and fine-tuning to implementation in production, monitoring and maintenance at high scale.

·      Building and managing ETL processes based on business and infrastructural needs.

·      Building and managing the MLOps system - the continues learning methodology to retrain models online.

·      Building and maintaining the CI/CD.

·      Model Monitoring – detect data and concept drifts in the data in real time and retrain new models to maximize performance.

·      Building and managing the analytics Feature Store.

·      Bring new ideas, take ownership and bring them to production, end to end.

Requirements:

           Must:

·      B.Sc. in Computer Engineering, statistics, Computer Science or equivalent.

·      5+ years hands-on experience in Python

·      3+ years hands-on experience in SQL

·      3+ years of experience with server-side technologies such as Node.js/Java/Linux etc.

·      Proved Experience in Model Monitoring.

·      Experience with Supervised and Unsupervised Learning.

·      Proved experience in implementing Machine Learning algorithms and building applications at scale in production.

·      Solid Git knowledge – Bitbucket or GitHub

·      Experience in planning and implementing CI/CD

·      Experience with Flow/pipeline ML Frameworks such as AirFlow, MLFlow, KubeFlow

·      Experience with Relational Databases such as MySQL and PostgreSQL.

·      Building ETL/ELT processes.

·      Experience in cloud computation (preferable Azure).

·      Solid Experience with Docker (Linux)- image creation and container orchestration

·      Experience with serverless functions – Azure Functions, AWS Lambda Functions, etc..

Bonus:

·      Experience with Edge devices orchestration – running our analytics on the edge device using tiny ML frameworks

·      Familiarity with Gen AI - a big advantage

·      Familiarity with Feature Store – a big advantage

·      Familiar with Databases - Redis, Influx, TimescaleDB, MongoDB– an advantage