Software Engineer I developing machine learning models and applications at Smart Data Solutions. Collaborating to improve infrastructure and automate processes using AI technology.
About the role
- ML Engineer developing ML Ops infrastructure and AI applications for Uni Systems. Collaborating closely with data scientists and developers in a hybrid work environment.
Responsibilities
- 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.
Requirements
- Master's degree in IT and minimum 15 years of relevant experience (or Bachelor's in IT and minimum 19 years of experience).
- One of the following: University degree in NLP (computer science or computational linguistics), Specialisation in (statistical/neural) machine translation (MT), or University degree in IT / Computer Science / Engineering with specialisation in AI, ML operations or data engineering.
- Strong experience managing on-prem and/or cloud-based MLOps infrastructure.
- Hands-on experience with containerisation and orchestration tools (e.g. Kubernetes, Docker, Podman, EKS, PKS).
- Proficient in MLflow, TensorFlow (TFX), and Airflow.
- Solid experience with AWS and/or Azure.
- Proficient in Python, Unix, and Bash scripting.
- Experienced in handling large-scale, unstructured data and data lakes.
- Familiar with monitoring/logging tools (e.g. ELK, Prometheus, Grafana, OpenTelemetry, CloudWatch).
- Skilled in model testing and validation in production environments.
- Good understanding of Agile development methodologies.
- Experienced with Infrastructure as Code (Terraform, CloudFormation).
- Knowledge of messaging platforms (Kafka, Redis, RabbitMQ).
- Understanding of data security (encryption, ML security a plus).
- Familiar with NoSQL databases (e.g. Elasticsearch, MongoDB, Cassandra, HBase).
- Experience with query languages (SQL, Hive, Pig) and information extraction.
- Excellent English (C-level, written and spoken).
Job title
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Degree requirement
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