Ml Engineer

Work Model:

Locations:

Job Purpose:

Analyzing, designing, developing, and managing the infrastructure to release scalable Data

Science models.

The ML Engineer is expected to deploy, monitor and operate production grade AI systems in a

scalable, automated, and repeatable way.

Job Responsibilities

• Create and maintain a scalable infrastructure to deliver AI/ML processes, responding to the user requests in near real time.
• Design and implement the pipelines for training and deployment of ML models.
• Design dashboards to monitor a system.
• Collect metrics and create alerts based on them.
• Design and execute performance tests.
• Perform feasibility studies/analysis with a critical point of view.
• Support and maintain (troubleshoot issues with data and applications).
• Develop technical documentation for applications, including diagrams and manuals.
• Working on many different software challenges always ensuring a combination of simplicity and maintainability within the code.
• Contribute to architectural designs of large complexity and size, potentially involving several distinct software components.
• Mentoring other engineers fostering good engineering practices across the department. Working closely with data scientists and a variety of end-users (across diverse cultures)

to ensure technical compatibility and user satisfaction.

• Work as a member of a team, encouraging team building, motivation and cultivating effective team relations.

Role Requirements E=essential, P=preferred

P - Bachelor's degree in Computer Science or related field

P - Masters degree in data engineering or related

E - Demonstrated experience and knowledge in Linux and Docker containers

E - Demonstrated experience and knowledge in some of the main cloud providers (Azure, GCP

or AWS)

P - Demonstrated experience and knowledge in distributed systems

E - Proficient in programming languages: Python

E Experience with ML/Ops technologies like Azure ML

E Self driving and good communication skills

P Experience with AI/ML frameworks: Torch, Onnx, Tensorflow

E - Experience designing and implementing CICD pipelines for automation.

P - Experience designing monitoring dashboards (Grafana or similar)

P - Experience with container orchestrators (Kubernetes, Docker Swarm.

E - Experience in using collaborative developing tools such as Git, Confluence, Jira, etc.