AI/Cloud Platform Engineer

JOB TITLE: AI/CLOUD PLATFORM ENGINEER

LOCATION: SCHAERBEEK, BELGIUM

LANGUAGES: ENGLISH, FRENCH OR DUTCH IS A BIG PLUS

WORK MODE: HYBRID (1 TO 2 DAYS ONSITE PER WEEK)

DURATION: ASAP - END OF 2026 (RENEWABLE)

CONTEXT AND OBJECTIVE OF THE MISSION

• The contract aims to technically strengthen the AI Platform Team in the realization, industrialization and operational support of a standardized, secure, scalable and cost-efficient enterprise AI platform.
• The AI Platform Engineer is responsible for the hands-on implementation of reusable platform services for generative AI, Retrieval Augmented Generation (RAG) and agentic AI. The contractor translates the defined architecture, standards and governance requirements into automated platform components that can be used by multiple development and platform teams.
• The assignment does not include ownership over business-specific use cases or business KPIs. The focus is on generic platform capabilities, self-service, technical quality assurance and the controlled transition from proof of concept to production.

Tasks and responsibilities

AI-platform engineering

• Deploy, configure, and maintain enterprise AI platform services, using AWS Bedrock as the primary platform foundation and additional cloud or on-premises integrations where required.
• Integrate and manage approved foundation models, model endpoints, inference services, runtimes, and supporting frameworks.
• Developing standardized APIs, SDKs, templates and reference implementations for the consumption of AI services.
• Contribute to lifecycle management, version control, compatibility, technical documentation and the controlled promotion of platform components between environments.

Agentic AI in integrations

• Develop and maintain reusable patterns for AI agents, tool use, planning and execution flows, and controlled interaction with enterprise services.
• Deploy and secure MCP servers and other standardized tool and service integrations.
• Developing agent templates and technical building blocks for identity propagation, authorization, error handling, time-outs and audit logging.
• Performing technical tests on reliability, safety and predictability of agent behavior.

RAG Platform Services

• Implementing generic retrieval and knowledge services for RAG applications.
• Integrating vector databases, knowledge bases, embedding services and retrieval components into the AI platform.
• Equipped with standardized interfaces for document and data access, respecting access rights, data classification and source reference.
• Collaborate with data engineers to connect data and knowledge pipelines to the AI platform.

DevOps, Automation and self-service.

• Automate provisioning, configuration, deployment, testing, and rollback through Infrastructure as Code and CI/CD.
• Develop and maintain Terraform modules, GitHub Actions workflows, and reusable deployment patterns.
• Equipped with paved-road workflows and self-service capabilities for team onboarding and use cases.
• integrating technical policy controls and quality controls into delivery pipelines.

Security, privacy and governance

• Implement technical guardrails for prompt and output control, sandboxing, network traffic, data access and tool use.
• Applying secrets management, key management, least privilege, encryption and secure configuration standards.
• Implementeren van policy-as-code, pre-deployment checks, audit logging en traceability.
• upporting security, privacy and compliance assessments with technical evidence and remediation.

Observability, operations en FinOps

• Setting up end-to-end monitoring and tracing for model calls, agent actions, toolcalls, errors, latency, policy hits and platform availability.
• Integrate with Dynatrace, OpenTelemetry, Langfuse, and specialized AI observability solutions where applicable.
• Supporting incident, problem and change processes, including root cause analysis and structural improvement actions.
• Measuring, reporting, and optimizing inference, API, compute, networking, and storage costs by platform service or use case.

Iced technical expertise

• Proven expertise in cloud and platform engineering within enterprise environments.
• In-depth working knowledge of AWS and experience with Amazon Bedrock or similar generative AI platform services.
• Strong programming knowledge in Python and experience in API and SDK development.
• Experience with generative AI, LLMs, RAG, embeddings, vector search, and agentic AI patterns.
• Experience with MCP, tool integrations or similar open integration standards is highly desirable.
• Experience with Terraform, GitHub Actions, CI/CD, GitOps, and automated quality checks.
• Knowledge of containers, Kubernetes and preferably Amazon EKS.
• Kennis van IAM, secrets management, policy-as-code, logging, monitoring en OpenTelemetry.
• Experience with Dynatrace, Langfuse, OpenSearch, LangGraph, LangChain or similar solutions counts as an added value.

Required behavioural competencies

• Takes technical ownership and works independently within established architecture and governance frameworks.
• Analytical and pragmatic problem solving with attention to reliability, safety and time-to-value.
• Strong collaboration skills in multidisciplinary teams.
• Coaching and enablement-oriented attitude, with a focus on reusable solutions instead of customization per team.
• Ability to clearly document and explain technical choices, risks and dependencies.
• Good oral and written communication in Dutch and English; knowledge of French is an added value.

Wanted profile

• At least five years of relevant experience in cloud engineering, platform engineering, DevOps or a similar technical domain.
• Proven experience building or managing production-ready shared platform services.
• Demonstrated experience with generative AI or AI/ML in a production context.
• Experience with hybrid or multicloud environments and collaboration with architecture, security, and compliance.
• Experience with Agile/Scrum and operational processes for incident, change and problem management.
• Relevant AWS, Kubernetes, security or AI certifications count as added value.

Expected results and deliverables

• Production-ready and documented AI platform components, APIs, SDKs, and deployment templates.
• Automated provisioning and delivery processes with integrated quality and policy controls.
• Operational monitoring, tracing, dashboards, alerts and runbooks for the platform services provided.
• Reusable patterns for agents, MCP integrations, RAG, and model consumption.
• Technical documentation, knowledge transfer and guidance of consuming teams.
• Demonstrable improvements in reliability, security, self-service and cost control.

Cooperation and reporting

• The contractor works under the functional direction of the responsible person of the platform team involved and reports on the progress, risks, dependencies and realized results to the responsible Service Delivery Manager. The contractor works closely with the relevant architecture, security, privacy, cloud, data and development functions.