This assignment 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 within an organization. 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 focus is on generic platform capabilities, self-service, technical quality assurance, and the controlled transition from proof of concept to production.
What you do
AI Platform Engineering:
- Implement, configure, and maintain enterprise AI platform services, with AWS Bedrock as the primary platform base and additional cloud or on-premises integrations where required
- Integrate and manage approved foundation models, model endpoints, inference services, runtimes, and supporting frameworks
- Develop 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 and Integrations:
- Develop and maintain reusable patterns for AI agents, tool use, planning and execution flows, and controlled interaction with enterprise services
- Implement and secure MCP servers and other standardized tool and service integrations
- Develop agent templates and technical building blocks for identity propagation, authorization, error handling, timeouts, and audit logging
- Perform technical tests on reliability, security, and predictability of agent behavior
RAG Platform Services:
- Implement generic retrieval and knowledge services for RAG applications
- Integrate vector databases, knowledge bases, embedding services, and retrieval components into the AI platform
- Provide standardized interfaces for document and data access, respecting access rights, data classification, and source referencing
- Collaborate with data engineers on connecting 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
Profile
- Demonstrable expertise in cloud and platform engineering within enterprise environments
- Thorough practical knowledge of AWS and experience with Amazon Bedrock or comparable generative AI platform services
- Strong programming knowledge in Python and experience with 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 desired
- 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
- Experience with Dynatrace, Langfuse, OpenSearch, LangGraph, LangChain, or similar solutions is a plus
- Minimum of five years of relevant experience in cloud engineering, platform engineering, DevOps, or a similar technical domain
- Demonstrable experience building or managing production-ready shared platform services
- Demonstrable experience with generative AI or AI/ML in a production context
- Experience with hybrid or multi-cloud environments and collaboration with architecture, security
- Experience with Agile/Scrum and operational processes for incident, change, and problem management
- Relevant AWS, Kubernetes, security, or AI certifications are a plus
- Takes technical ownership and works autonomously within defined architecture and governance frameworks
- Analytical and pragmatic problem-solving with attention to reliability, security
- Strong collaboration skills in multidisciplinary teams
- Coaching and enablement-oriented attitude, focusing on reusable solutions rather than custom solutions per team
- Ability to clearly document and explain technical choices, risks
- Good oral and written communication in Dutch and English; knowledge of French is a plus
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