DESCRIPTION OF THE TASKS

• Design, implement and maintain a scalable, reliable and secure hybrid cloud ML Ops infrastructure to deploy, test, manage and monitor ML models in different environments;

• Development and maintenance of software applications in the field of Natural Language Processing (NLP), Machine Learning (ML), Deep Learning (DL) and/or Artificial Intelligence (AI);

• Work closely with data scientists and back-end developers to build, test, integrate and deploy ML models;

• Analyse performance metrics and troubleshoot issues to ensure high availability and reliability;• Design CI/CD pipelines, use orchestration solutions and data versioning tools;

• Creating automated anomaly detection systems and constant tracking of its performance and

optimising ML pipelines for scalability, efficiency and cost-effectiveness.;

• Design the IT architecture for solutions in the NLP / ML / AI fields, and coordinate its

implementation considering master- and meta-data management concepts;

• Provision of security studies, security assessments or other security matters associated with

information system projects;

• Provision of support and guidance to other team members on MLOps practices.

LEVEL OF EDUCATION

Master degree of 5 years.

At least one of the following:

Advanced university degree in natural language processing (computer science or computational linguistics;

A specialisation in (statistical/neural) machine translation (MT), or equivalent experience;

University degree in IT / Computer Science / Engineering or equivalent with specialisation in artificial intelligence, ML operations or data engineering.

KNOWLEDGE AND SKILLS

• Excellent knowledge of managing an on-prem and/or cloud MLOps infrastructure.

• Excellent knowledge of containerisation and orchestration platforms (e.g. Kubernetes, Docker,

Podman, EKS, PKS) Good knowledge of MLflow, TensorFlow (TFX) or equivalents.

• Good knowledge of Airflow

• Good knowledge of AWS and/or Azure.

• Good knowledge of Python.

• Good knowledge of Unix and Bash.

• Good knowledge agile software development methodologies.

• Good knowledge of infrastructure as code (Terraform, CloudFormation)

• Good knowledge of messaging services and platforms (e.g. Kafka, Redis, RabbitMQ).

• Knowledge of data security measures (knowledge of encryption mechanisms and ML security is considered a plus).

• Knowledge of NoSQL databases, such as Elasticsearch, MongoDB, Cassandra, HBase, etc.

• Knowledge of query languages, such as SQL, Hive, Pig, etc. and with information extraction.

• Experience with data analytics over big datasets, non-structured databases as well as data lakes.

• Experience with monitoring and logging tools (e.g. ELK stack, Prometheus, Grafana,

OpenTelemetry, Cloudwatch)

• Experience with model testing and model validation in production environments

• Ability to write clear and structured technical documentation

• Excellent knowledge of on-prem or cloud solutions for data science applications.

• Ability to give business and technical presentations.

•Capability to write clear and structured technical documents.

• Ability to participate in technical meetings and good communication skills.

Optional certifications:

• AWS Certified Machine Learning.

• Microsoft Azure AI Engineer Associate