MLOps Engineer (SC Clearance)

At CGI, you will play a pivotal role in enabling the deployment and operation of advanced AI systems that support critical national security outcomes. Working within our Space, Defence and Intelligence business unit, you will help design and deliver secure, scalable machine learning platforms that power innovation across mission-critical environments. We combine deep sector expertise with emerging technologies and collaborative delivery to create impactful solutions, giving you the opportunity to take ownership of complex challenges, contribute creative ideas, and grow within a supportive, expert-led community.



The Space, Defence and Intelligence business unit in CGI is a true IT Systems Integrator. We work, build, and operate bespoke, technically complex, mission-critical systems which help our clients keep us all safe and secure. We bring innovation to our clients using proven and emerging technologies, agile delivery processes and our deep expertise across the breadth of space, defence, intelligence, aerospace and maritime, all underpinned by our end-to-end cyber capability. We work collaboratively with global technology companies, cutting edge SMEs and academia to deliver the optimal solution for each client.



CGI was recognised in the Sunday Times Best Places to Work List 2025 and has been named a UK ‘Best Employer’ by the Financial Times. We offer a competitive salary, excellent pension, private healthcare, plus a share scheme (3.5% + 3.5% matching) which makes you a CGI Partner not just an employee. We are committed to inclusivity, building a genuinely diverse community of tech talent and inspiring everyone to pursue careers in our sector, including our Armed Forces, and are proud to hold a Gold Award in recognition of our support of the Armed Forces Corporate Covenant. Join us and you’ll be part of an open, friendly community of experts. We’ll train and support you in taking your career wherever you want it to go.



Due to the secure nature of the programme, you will need to hold UK Security Clearance or be eligible to go through this clearance. This position will be based on site near Gloucester 5 days a week.

Profil recherché

In this role, you will design, build and operate secure AI and machine learning platforms within highly controlled environments. You will take ownership of developing the infrastructure and automation required to deploy, monitor and scale models in production, including large language models and other advanced AI systems. Working closely with multidisciplinary teams, you will contribute to delivering resilient, high-performance platforms that enable critical decision-making and operational effectiveness.



You will play a key role in shaping how AI is operationalised in air-gapped environments, bringing innovative approaches to platform engineering while being supported by experienced colleagues and a collaborative culture focused on continuous improvement.



• Design & Deliver secure, scalable MLOps platforms for production environments



• Build & Optimise air-gapped machine learning pipelines and workflows



• Deploy & Manage LLM inference clusters and AI model hosting platforms



• Implement & Enhance model lifecycle processes, including monitoring and retraining



• Develop & Automate infrastructure using DevSecOps principles and tooling



• Monitor & Improve system performance using observability tools and best practices



• Collaborate & Innovate with cross-functional teams to deliver mission-critical solutions

Required qualifications to be successful in this role

You should have experience building and operating production-grade machine learning platforms, with strong technical skills across software development, infrastructure, and cloud-native tooling. A solid understanding of Linux environments and modern MLOps practices is essential, along with the ability to work effectively in secure, constrained environments.



• You should have experience developing in Python for data or platform engineering



• Strong proficiency in Linux systems and command-line environments



• Experience with MLOps tools such as MLflow, Kubeflow or Airflow



• Familiarity with monitoring tools such as Prometheus or Grafana



• Experience with containerisation and orchestration (Docker, Kubernetes, Terraform)



• Understanding of CI/CD pipelines and DevSecOps practices



• Experience with platforms such as VMware, OpenShift or OpenStack (desirable)



• Exposure to AI/ML workloads, GPU environments, or secure/air-gapped systems (advantageous)





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