Overview
We\'re looking for an MLOps Engineer who cares about building products and capabilities that really work in the field. At Thales, you\'ll be empowered to create enduring solutions with broad impact, not just to develop prototypes or chase patents. Our structure supports long-term technical career growth, offering clear and supported paths for specialists and experts—so you can achieve mastery and broad influence without giving up hands-on technical work. You\'ll enjoy both depth and breadth in your work, collaborating directly with business and technical teams.
Responsibilities
* Primary Purpose of the Role: Drive the evolution, deployment, and scaling of AI and Data capabilities within Thales and for our customers, increasing both business growth and customer satisfaction through reliable and innovative solutions.
* Set up and configure ML environments and deployment tools such as Kubernetes, Docker, and Linux Containers.
* Write scripts to automate workflows and ensure the reproducibility of ML experiments and deployments.
* Conduct regular performance reviews and data audits of deployed models.
* Influence and collaborate with data scientists, championing best practices to efficiently translate concepts into robust product features.
* Troubleshoot issues related to model performance and technical infrastructure.
* Provide support and training to team members on MLOps tools and practices; champion knowledge sharing and reusable patterns.
* Create and maintain CI/CD pipelines for automating development, test, and deployment processes.
* Identify and drive the adoption of reusable solutions—enabling cost-effective delivery across multiple projects and teams.
* Collaborate with development and IT teams to deliver secure, scalable, and high-quality applications on the cloud, leveraging automation tools and scripts.
* Work with product owners to refine and meet user needs.
* Contribute actively to agile threat modelling and vulnerability management.
* Ensure the compliance of solutions with security and regulatory requirements—especially high assurance, MOD-related software.
* Support Customer/Solution Data Architects with data landscape coordination and cataloguing.
* Support and implement 3rd party data integrations for a wide range of use cases (internal and external).
Qualifications and Experience
* Experience in MLOps, DevOps, DevSecOps, or related software or AI engineering roles in complex, regulated environments.
* Proficiency with CI/CD deployment processes.
* Track record of deploying software/AI solutions in complex programmes, with an emphasis on operational reliability and scalability.
* Strong technical documentation and logical analytical skills—capable of problem-solving at both systems and detailed technical levels.
* Experience working on Linux-based infrastructure.
* Excellent knowledge of Python and at least one modern programming language for automation and ML development (e.g. Java).
* Experience with PyTorch and familiarity with other ML frameworks or libraries as needed (e.g., Scikit-learn, etc.).
* Understanding of supervised and unsupervised ML techniques and algorithms.
* Experience with model monitoring, logging, and performance evaluation tools (e.g., MLflow, Prometheus).
* Strong scripting skills in Bash, PowerShell, or similar for workflow automation.
* Experience with Linux Containers and orchestration platforms.
* Proficient in version control systems like Git.
* Ability to design secure, innovative solutions while adhering to strict security and assurance standards.
* Self-motivated to contribute, enable, influence, and inspire colleagues in technical fields.
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