AI Platform engineer

pact digital is an international consulting and engineering group that supports its clients in their digital transformation projects. /ppPresent in 12 countries and with more than 7,000 employees, we leverage our expertise to address their challenges in software development, cybersecurity, data, cloud, and AI. /ppOur ambition: to become the trusted technology partner of the most innovative companies, by designing and securing systems that enhance their performance and resilience. /ppJoining act digital means becoming part of an agile and committed organization that works closely with its clients to turn ideas into concrete results, with pragmatism and high standards. /ph3Job Description /h3ulliBuild and operate the platform's container runtime across environments (dev, acc, pro): cluster lifecycle, node provisioning and autoscaling, upgrades, storage and cost. /liliOwn service-to-service networking and traffic management: ingress, mutual TLS, routing, network and admission policies that enforce the platform's security and high-availability standards. /liliDesign and maintain the infrastructure as code that defines the platform, its automated tests, and the CI/CD pipeline that validates and applies changes per environment. /liliPackage and deploy platform components and controllers: the model gateway and its data stores, the isolated runtime for agent code, DNS, certificate and secrets integrations. /liliImplement identity and access end to end: workload identities, federation with the corporate identity provider and with external clouds, user login and token-based access for machine clients, secrets management. /liliBuild and operate the controlled public entry to the platform: edge load balancing, web application firewall, DNS and certificates, and the rules that decide what is reachable from where. /liliMake the platform observable: logs, metrics and alerts for the runtime, the network and the AI workloads, including model usage and cost. /liliOnboard use-case teams: provide the patterns, isolation boundaries, policies and documentation they need to run agents and tool integrations (e.g. MCP-based) safely on the platform, each backed by a documented, validated value case. /liliContribute to technical designs and architecture decisions together with the tech lead and architect, and keep the documentation, diagrams and decision records in step with the implementation. /liliEmbed security, responsible AI guardrails, governance and compliance by design (including AI Act considerations), in collaboration with architecture and data governance. /liliMonitor platform performance, reliability and cost, and contribute to ongoing optimisation. /li /ulh3Profile / Requirements /h3ulliOperating a container platform in production (Kubernetes or equivalent): workloads, networking, access control, upgrades, troubleshooting. /liliService mesh and traffic management concepts: gateways, mutual TLS, routing, authorization policies. /liliInfrastructure as code (Terraform or equivalent): reusable modules, state across environments, automated testing. /liliContainer fundamentals and packaging (Docker, Helm or equivalent, image registries). /liliCloud platforms, AWS strongly preferred: networking, IAM, load balancing, WAF, DNS, certificates, key and secrets management. /liliSecurity and identity: OIDC and SAML federation, workload identity, secrets management, secure-by-design integration patterns. /liliCI/CD with Git (GitLab or similar), working through merge requests and code review. /liliObservability: metrics, logs, tracing and alerting with a cloud-native or vendor stack. /liliScripting in Python or Bash for tooling and automated checks. /liliStrong analytical and problem-solving abilities. Strong documentation and communication skills; comfortable writing architecture decision records. /liliFluent in English (verbal and in writing), Dutch or French optional. /li /ulh3Nice to have /h3ulliRunning model gateways or model access components (e.g. LiteLLM, AWS Bedrock, Azure OpenAI). /liliSandboxing and workload isolation (e.g. gVisor, Kata Containers, seccomp). /liliAgentic AI concepts and tool integrations (e.g. MCP), seen from the hosting side. /liliRAG, embeddings and vector stores; data platforms and warehouses such as Snowflake. /liliPolicy as code (e.g. OPA, Kyverno, Kubernetes admission policies). /liliData governance, security frameworks and AI Act / responsible AI principles. /liliWorking with AI coding assistants in a reviewed, test-driven workflow. /liliExperience with agile methodologies and DevOps practices. /li /ul