AI Platform Engineer

Functie

1. Context and Objective

The assignment is aimed at strengthening the AI Platform Team in the implementation, industrialisation and operational support of a standardised, 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 role translates defined architecture, standards and governance requirements into automated platform components that can be consumed by multiple development and platform teams.

The focus is on generic platform capabilities, self-service, technical quality assurance and the controlled transition from proof of concept to production.

2. Key Responsibilities

AI Platform Engineering

• Implement, configure and maintain enterprise AI platform services, with AWS Bedrock as the primary platform.

• Integrate and manage approved foundation models, model endpoints, inference services, runtimes and supporting frameworks.
• Develop standardised APIs, SDKs, templates and reference implementations.
• Contribute to lifecycle management, versioning, compatibility and technical documentation.

Agentic AI & Integrations

• Develop reusable patterns for AI agents, tool use, planning and execution flows.
• Implement and secure MCP servers and other standardised tool/service integrations.
• Develop agent templates covering identity propagation, authorisation, error handling, time-outs and audit logging.
• Perform technical testing of agent reliability, security and predictability.

RAG Platform Services

• Implement generic retrieval and knowledge services for RAG applications.
• Integrate vector databases, knowledge bases, embedding services and retrieval components.
• Provide standardised interfaces for document and data access, respecting access rights and data classification.
• Collaborate with data engineers on data and knowledge pipelines.

DevOps, Automation & Self-Service

• Automate provisioning, configuration, deployment, testing and rollback using Infrastructure as Code and CI/CD.
• Develop and maintain Terraform modules and GitHub Actions workflows.
• Provide paved-road workflows and self-service capabilities for onboarding teams and use cases.
• Integrate policy and quality controls into delivery pipelines.

Security, Privacy & Governance

• Implement technical guardrails for prompt/output control, sandboxing, networking, data access and tool usage.
• Apply secrets management, key management, least privilege and encryption.
• Implement policy-as-code, pre-deployment checks, audit logging and traceability.
• Support security, privacy and compliance assessments and remediation.

Observability, Operations & FinOps

• Set up monitoring and tracing for model calls, agent actions, tool calls, errors, latency and platform availability.
• Work with Dynatrace, OpenTelemetry, Langfuse and other AI observability solutions where applicable.
• Support incident, problem and change management, including root cause analysis.
• Monitor and optimise AI platform and infrastructure costs.

3. Required Technical Expertise

• Strong experience in cloud and platform engineering within enterprise environments.
• Strong practical knowledge of AWS and experience with Amazon Bedrock or comparable GenAI platforms.
• Strong Python programming skills and experience with API/SDK development.
• Experience with Generative AI, LLMs, RAG, embeddings, vector search and Agentic AI.
• Experience with MCP, tool integrations or comparable standards is highly desirable.
• Experience with Terraform, GitHub Actions, CI/CD, GitOps and automated quality controls.
• Knowledge of containers, Kubernetes and preferably Amazon EKS.
• Knowledge of IAM, secrets management, policy-as-code, logging, monitoring and OpenTelemetry.
• Experience with Dynatrace, Langfuse, OpenSearch, LangGraph or LangChain is a plus.

4. Required Competencies

• Takes technical ownership and works independently within defined architecture and governance frameworks.
• Analytical and pragmatic problem-solving approach, with focus on reliability, security and time-to-value.
• Strong collaboration skills in multidisciplinary teams.
• Enablement-oriented mindset, focusing on reusable solutions rather than team-specific customisation.
• Able to clearly document and communicate technical decisions, risks and dependencies.
• Good communication skills in Dutch and English; French is a plus.

5. Desired Profile

• Minimum 5 yea