Senior ML and AI DevOps Engineer (MLOps)

As a Senior ML and AI DevOps Engineer / MLOps Engineer, you will join a newly forming AI Hub with the mission to build the deployment, monitoring, and operational foundation behind scalable AI tools and platforms. Your work will directly shape how AI services are deployed, governed, observed, and maintained across on-prem and cloud environments.

flatexDEGIRO SE operates a leading online brokerage platform in Europe, supported by modern in-house technology. This technological foundation enables the company to continuously improve its service offering and maintain high standards in product, price, and platform quality.

This is not a role focused on keeping experimental models alive or supporting isolated prototypes. We are moving AI into production, which means our services need to be treated like core enterprise platforms. They must be reliable, observable, secure, governed, and ready for large-scale adoption.

We are looking for a builder who understands that production AI is not only about models. It is about deployment discipline, automation, rollback strategies, observability, reliability, and operational readiness. Someone who thinks beyond the pipeline, challenges existing deployment patterns, and brings practical ideas that help us move faster while staying secure and compliant.

You should actively use AI in your own work to improve automation, troubleshooting, documentation, and delivery speed, while helping us shape the operational standards for AI across the company.

This is what you’ll do

As a Senior ML and AI DevOps Engineer / MLOps Engineer, you will focus on deployment, operationalization, CI/CD, observability, reliability, governance, and production operations for AI workloads. Your work will ensure that AI services can move safely from prototype to controlled production deployment.

You will:

• Build, maintain, and improve CI/CD pipelines for AI services and ML workloads.
• Implement secure deployment, release, and rollback strategies using Kubernetes, Helm, Docker, and related tooling.
• Establish observability and monitoring for AI services, including accuracy, performance, reliability, telemetry, and cost.
• Support the full AI lifecycle from prototype to production handover in a controlled and governed way.
• Define and maintain runbooks, playbooks, operational standards, and deployment documentation for AI services.
• Integrate AI workloads with existing monitoring, CI/CD, infrastructure, security, and IT control processes.
• Work closely with security, IT operations, architecture, and engineering teams to align AI workloads with governance frameworks.
• Help standardize how AI services are deployed, tested, monitored, evaluated, and maintained across environments.
• Identify opportunities to reduce manual effort, improve automation, and increase operational stability.
• Support on-prem, hybrid, and cloud-connected AI delivery models where appropriate.
• Actively use AI in your own work to improve automation, troubleshooting, testing, documentation, and engineering efficiency.

You’ll bring us these skills

We are looking for an experienced DevOps, Platform, or MLOps Engineer who can bring production discipline to AI delivery. You should be pragmatic, ownership-driven, and comfortable building operational standards while the environment is still maturing.

You bring:

• 6+ years of experience in DevOps, platform engineering, infrastructure engineering, MLOps, or a similar technical role.
• Strong hands-on experience with Kubernetes, Helm, Docker, and containerized deployments.
• Solid experience building and managing CI/CD pipelines for production services.
• Experience with secure deployment strategies, release management, rollback mechanisms, and operational automation.
• Good understanding of observability, monitoring, telemetry, performance tracking, and production reliability.
• Familiarity with machine learning lifecycle principles, including model deployment, evaluation, monitoring, and controlled production handover.
• Experience operating in regulated, security-conscious, or enterprise IT environments.
• Strong understanding of governance, access control, compliance, and internal IT control requirements.
• Ability to work closely with security, infrastructure, architecture, and engineering teams.
• Experience deploying AI, ML, or data workloads at scale would be considered an advantage.
• Knowledge of hybrid infrastructure, on-prem environments, and secure network design would be a plus.
• Familiarity with Microsoft Copilot, Azure OpenAI Service, or similar governed AI environments would be beneficial.

Most importantly

You bring a builder’s mindset. You do not only maintain pipelines or follow predefined processes. You help create the standards, improve the delivery model, and make sure AI services can scale safely, reliably, and with real business impact. You balance speed with engineering discipline and understand what it takes to turn AI from experimentation into a trusted enterprise capability.

What we offer

• Promise of a good work-life balance
• 30 days paid leave
• An enthusiastic and fun team to work with
• Personal and professional development with access to relevant courses
• Transport allowance or related benefits like a leased electric bike
• Pension plan
• A challenging job in an international and fast-growing company
• A competitive salary
• A lot of freedom executing your role and a chance to shape your own career
• The opportunity to participate in a new, enthusiastic team of likeminded professionals
• Freedom to work flexibly
• Company laptop and options to make your home workspace comfortable, such as a chair, screen and headphones
• Gym membership options