## Join our Team
About this opportunity
Join our team as a Senior AI and DevOps Engineer, helping build secure, reliable and scalable AI solutions for enterprise environments. You will combine practical experience in AI/ML, Generative AI, cloud platforms and automation to develop solutions that move effectively from experimentation into production. This role offers the opportunity to work across AI applications, deployment automation, infrastructure, security and operations while contributing to continuous improvement in a collaborative environment.
What you will do
- Develop and integrate AI/ML and Generative AI applications using Python, large language models and frameworks such as PyTorch or TensorFlow.
- Build Retrieval-Augmented Generation solutions using embeddings, vector databases, semantic search and AI agents.
- Implement tool calling, Model Context Protocol (MCP) integrations and appropriate controls for reliable AI applications.
- Create and maintain automated CI/CD pipelines for testing, security checks, packaging, deployment and validation.
- Automate infrastructure provisioning and configuration using Ansible, Python and Bash.
- Deploy and manage cloud-native applications using Docker, Kubernetes and AWS, Azure or Google Cloud.
- Apply security controls, access management, secrets handling and responsible AI practices, including human review where appropriate.
- Improve monitoring, logging, performance, reliability and troubleshooting across development and production environments.
- Work with engineering, architecture and product teams to deliver maintainable solutions and resolve technical challenges.
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The skills you bring
- 5 to 8 years of relevant experience in AI/ML engineering, DevOps, cloud engineering or automation.
- Strong Python programming skills and practical experience with Generative AI, large language models, prompt design, Retrieval-Augmented Generation, embeddings, vector databases and AI agent frameworks.
- Experience with PyTorch or TensorFlow.
- Hands-on experience with Git, Ansible, CI/CD pipelines, Python and Bash scripting, and JSON or YAML. Experience with GitLab is an advantage.
- Experience using Docker, Kubernetes and at least one major cloud platform: AWS, Azure or Google Cloud.
- Knowledge of code quality and security tools such as SonarQube or SAST, artifact repositories, container registries and secure access solutions.
- Experience working with Linux environments, including Ubuntu and Red Hat Enterprise Linux, as well as configuration management, networking, monitoring, logging and production support.
- A bachelor’s or master’s degree in computer science, engineering or a related technical discipline.
- Experience in telecommunications, networking or complex enterprise environments is an advantage.
- A collaborative, learning-oriented approach and the ability to communicate clearly with both technical and non-technical stakeholders.
