Senior AI Platform Engineer / AI Architect - Microsoft Technologies
We're looking for a Senior AI Platform Engineer / AI Architect to join our team in the Netherlands in a hybrid working mode. In this role, you will design and build production-grade AI systems on Microsoft Azure while enabling engineering teams to adopt AI-native software engineering practices. This is a hands-on role that involves coding, infrastructure-as-code (IaC), CI/CD pipelines, test automation, observability and ensuring operational readiness at scale.
This position is ideal for a senior engineer with deep experience in cloud-native engineering who is now focused on GenAI, RAG, agents, GitHub Copilot, AI gateways, evaluation and platform automation. Unlike conceptual or short-term assignments, this role allows you to contribute to enterprise AI transformation programs that impact real business workflows such as engineering productivity, intelligent automation and enterprise delivery.
You’ll work closely with architects, engineers, GitHub/Microsoft specialists and client delivery teams on solutions that require both speed and production discipline. This role goes beyond basic API integrations and involves designing AI agent architectures, implementing evaluation frameworks, securing delivery pipelines and establishing robust governance. The focus is on real enterprise AI delivery, not isolated prototypes—building systems that can be tested, deployed, monitored, secured and transitioned for operational use.
Responsibilities
- Build RAG pipelines, AI assistants, agent workflows, AI gateway components, MCP-based integrations and reusable platform elements
- Implement Azure AI solutions using Microsoft Foundry, Azure OpenAI, Azure AI Search, Azure Functions, Azure API Management, AKS, Azure Container Apps, Key Vault, Cosmos DB and monitoring tools
- Deliver AI-native engineering workflows using GitHub Copilot, GitHub Actions, GitHub Advanced Security and secure pull-request patterns
- Apply harness engineering patterns for AI systems, including agent instructions, tool contracts, retrieval grounding, evaluation suites, telemetry, safety checks, cost tracking and human approval gates
- Build CI/CD pipelines, IaC automation, observability stacks and deployment frameworks for AI workloads
- Create reusable templates, reference implementations, demos and onboarding kits for client teams
- Own one or more implementation workstreams such as retrieval, orchestration, AI gateways or Copilot enablement
- Mentor engineers, review code and support architecture decisions for AI-assisted engineering adoption
Requirements
- 6+ years of experience in software engineering, cloud engineering, DevOps or platform engineering
- Hands-on experience with GenAI, RAG, agentic systems, copilots and AI platform automation
- Proven coding skills in Python, TypeScript, C#, Java or Go for production-grade APIs, back-end services and infrastructure automation
- Strong experience with Microsoft Azure and cloud-native architectures
- Knowledge of containers, Kubernetes, serverless models and Infrastructure-as-Code practices
- Familiarity with security, identity, logging, monitoring, cost optimization and operational readiness principles
- Ability to demonstrate production-ready AI systems: tested, deployed, monitored and supported at scale
- Practical experience mentoring teams and guiding AI-assisted SDLC with quality and governance in mind
- Strong communication and collaboration skills adaptable to both technical and business environments
Nice to have
- Deep knowledge of Microsoft Foundry, Azure OpenAI, Azure AI Search and related Azure AI services
- Experience with GitHub toolchains including Copilot, GitHub Actions, Advanced Security and MCP-based integrations
- Practical knowledge of RAG implementations: hybrid search, embeddings, semantic ranking and evidence capture
- Familiarity with agent frameworks such as Semantic Kernel, Microsoft Agent Framework, LangChain, AutoGen and similar
- Strong programming skills in multiple languages (Python, TypeScript, C#, Java, Go)
- Experience in evaluation and agent reliability engineering: golden datasets, regression tests, prompt-injection defense, state handling, retries, recovery and feedback loops
- Exposure to Microsoft 365 Copilot integrations (Graph, Teams, SharePoint, Power Platform)
- Familiarity with other AI ecosystems like AWS Bedrock, Google Vertex AI, Databricks, Anthropic, Hugging Face or Snowflake
