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 op