AISB-774 Senior MLOps / AI Engineer

Position Summary

We are looking for a Senior MLOps / AI Engineer to design, deploy and maintain secure, scalable and highly available hybrid MLOps infrastructures. The role sits at the crossroads of cloud computing, AI, machine learning and large‑scale data architectures within a demanding technical environment.

Key Responsibilities

• Design, implement and maintain a scalable, reliable and secure hybrid MLOps infrastructure to deploy, test, manage and monitor ML models across multiple environments.
• Develop and maintain software applications in NLP, Machine Learning, Deep Learning and Artificial Intelligence.
• 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 and maintain CI/CD pipelines, orchestration solutions and data versioning tools.
• Build automated anomaly detection systems, continuously monitor performance and optimise ML pipelines for scalability, efficiency and cost‑effectiveness.
• Design IT architectures for NLP / ML / AI solutions and coordinate their implementation, taking master‑ and metadata management concepts into account.
• Deliver security studies, security assessments and address other security aspects related to information system projects.
• Provide guidance, support and expertise to team members on MLOps best practices.

Required Qualifications

• Master’s degree in IT, Computer Science, Engineering or equivalent with specialization in AI, MLOps or data engineering, or an advanced degree in NLP, computational linguistics or machine translation.
• Minimum 13 years of professional IT experience.
• Excellent knowledge of managing on‑premise and/or cloud‑based MLOps infrastructures.
• Strong expertise in containerization and orchestration platforms (Kubernetes, Docker, Podman, EKS, PKS).
• Good knowledge of MLflow, TensorFlow (TFX) or equivalent frameworks.
• Good knowledge of Airflow.
• Strong knowledge of AWS and/or Azure.
• Excellent Python skills.
• Good knowledge of Unix and Bash.
• Good understanding of Agile software development methodologies.
• Good knowledge of Infrastructure as Code (Terraform, CloudFormation).
• Good knowledge of messaging platforms (Kafka, Redis, RabbitMQ).
• Knowledge of data security, encryption mechanisms and ML security (considered a plus).
• Knowledge of NoSQL databases (Elasticsearch, MongoDB, Cassandra, HBase).
• Knowledge of query languages (SQL, Hive, Pig) and information extraction techniques.
• Experience with big data analytics, unstructured databases and data lakes.
• Experience with monitoring and logging tools (ELK stack, Prometheus, Grafana, OpenTelemetry, CloudWatch).
• Experience with model testing and validation in production environments.
• Ability to produce clear, structured technical documentation.
• Excellent communication skills with both technical and non‑technical stakeholders.
• Strong analytical, problem‑solving skills and ability to adapt to rapidly evolving technologies.

Preferred Certifications

• AWS Certified Machine Learning.
• Microsoft Azure AI Engineer Associate.