Job Description

OF THE TASKS

Following tasks will be performed by external service provider:

•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;

•Analyze 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.

Requirements

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.

•Ability to apply high quality standards.

•Ability to cope with fast changing technologies.

•Very good communication skills with technical and non-technical audiences.

•Analysis and problem solving skills.

•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.

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