Senior MLOps Engineer

Location: Brussels

Preferred Start Date: ASAP

Language: English

Employment Type: Full-time

Work Arrangement: Hybrid (2 days on-site per week, preferably Monday and Thursday)

Key Responsibilities:

Engineering & Technical Leadership

• Lead technical decisions for AI operational workloads, including deployment, automation, monitoring, drift detection, and retraining pipelines.
• Ensure adherence to modern software engineering best practices, including code quality, testing, CI/CD, observability, and security-by-design.
• Review and guide Python codebases to ensure maintainability and performance.
• Provide mentorship to engineers and support continuous improvement within the squad.

Operational Excellence & Reliability

• Oversee the full operational lifecycle of AI models, including deployment, monitoring, performance tracking, drift detection, and retraining cycles.
• Guarantee availability and reliability targets for live models.
• Participate in the on-call rotation and drive incident response, mitigation, and RCA improvements.

Security, Risk & Compliance

• Ensure all squad deliverables comply with operational and security standards.
• Enforce secure practices for deployment, secret management, access control, and data handling.
• Collaborate with governance, security, and platform teams to align on internal policies and controls.

Automation & Infrastructure Management

• Lead the squad’s implementation of automation across the stack, including Jenkins CloudBees, CDRO, Ansible, and Helm-based deployments into OpenShift.
• Manage secret management via Vault and storage on S3 Cloudian.
• Monitor via Ansible, dashboards, and operational metrics.
• Identify opportunities to increase automation maturity, such as fully automated retraining and automated drift detection.

Cross-Squad Collaboration

• Work closely with platform/infrastructure squads, data science squads, and governance, security, responsible AI, and architecture teams.
• Translate tribal OKRs into actionable technical plans for the squad.

Required Skills & Experience:

Bachelor’s or Master’s degree in Computer Science, Software Engineering, Information Security, Data Engineering, or a related technical field.

Core Technical Skills:

• Strong hands-on experience in Python for production-grade systems.
• In-depth knowledge of Kubernetes/OpenShift and Helm deployment patterns.
• Experience with CI/CD pipelines using Jenkins CloudBees and CDRO.
• Strong understanding of Ansible for automation and orchestration.
• Experience with Vault for secrets management and secure configuration.
• Solid understanding of object storage (S3/Cloudian) and file-based AI workloads.

Security & Compliance:

• Proven experience applying secure engineering standards in production environments.
• Knowledge of operational risk controls, incident processes, and audit/traceability requirements.

AI/ML Lifecycle (Nice to Have but Strongly Appreciated):

• Experience working with data science workflows, including model training, inference, and validation.
• Understanding of drift detection, model retraining triggers, and automated pipelines for ML model lifecycle.
• Exposure to MLOps principles and production practices.

Leadership & Collaboration:

• Demonstrated experience providing technical leadership within a squad or engineering team.
• Strong communication skills to interact across architecture, data science, security, and Ops layers.
• Ability to translate complex technical topics into actionable squad-level initiatives.