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# Sr. technical solutions architect, ai/data - bilingual

- **Company:** Softchoice
- **Location:** Bleskensgraaf, Netherlands
- **Experience:** Expert
- **Salary:** €10,313.0 - €12,892.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, BigQuery, Code Review, Customer Data Management, OAuth, Open Source Technology, OpenID, OpenShift, Inference Optimization, OpenAI, Security Assertion Markup Language (SAML), Software Engineering, Systems Integration, Enterprise Data Management, Private Cloud Environment, Graphics Processing Unit (GPU), Google Cloud, Retrieval-Augmented Generation, Snowflake, Multi-Agent Systems, Model Validation, Generative AI, Discretization, AI Platforms, Kubernetes, Information Technology, Low Latency, Apache Kafka, Graphql, Ollama, Machine Learning Operations, Nim (Programming Language), Google Gemini, Graph RAG, Model Context Protocol, VLLM, Databricks, Agent2Agent Protocol
- **Published:** November 3, 2026
- **Apply:** https://nl.talent.com/redirect?id=643726675782672359&source=vacant&utm_medium=xml&utm_campaign=marketing&utm_product=vacantnew_bulk&oapply=org_v2026-10&puid=edaaaddcddadadd9bdde9ddaeddaadd9adabbdd99dd89ddebddaadd9bdaeadddcddb9dd7bea3addffddgaed2dddgaddgfddgaddgaddg9ddg6ed3gc9bbb9b3bee3ee33def3de93de73dedgddcgddafddd3de83de7gddf3ee33de93deg3ee33ee33deg3ee33ee33ee33ee33ee33ee3&pos=2&xml_batch_generation_id=e67cf5a7-adfd-4e07-8730-7ac476f6b525&falcon=1&pid=4c22a930cf1f&bpid=236ffcebae5aea352e2f48eb781d7c0c

## About the Role

* 8+ years of progressive experience in software engineering, solutions / Enterprise architecture, or applied AI/ML, with at least 2+ years in a hands-on Generative AI or agentic AI role.
 * Professional fluency in French and English, written and spoken (required) - able to lead client workshops, executive presentations, and technical documentation in both languages.
 * Experience designing sovereign / on-premise AI solutions, including private model serving (NVIDIA NIM / NeMo, vLLM, TGI, Ollama), enterprise AI platforms (NVIDIA AI Enterprise, Red Hat OpenShift AI, Dell AI Factory, HPE Private Cloud AI, Run:ai), and on-premise vector / graph stores.
 * Solid understanding of the infrastructure behind on-premise AI: GPU architecture and capacity planning, inference optimization (quantization, batching, KV-cache management), high-performance networking and storage, and Kubernetes-based GPU scheduling.
 * Strong understanding of data sovereignty requirements and controls - data residency, privacy regulation (e.g., PIPEDA, Quebec Law 25, GDPR), air-gapped and disconnected deployments - and the ability to articulate trade-offs between sovereign, hybrid, and public-cloud AI architectures.
 * Deep knowledge with at least one of the three major hyperscaler AI platforms (AWS, Azure, GCP) and a working understanding of the second and third.
 * Production experience designing and shipping RAG and/or agentic systems, including practical familiarity with chunking strategies, embedding model selection, retrieval evaluation, and orchestration patterns.
 * Working knowledge of MCP (Model Context Protocol) and modern agent-tool integration patterns; ability to design MCP servers and clients, and to reason about when MCP is the right abstraction versus alternatives.
 * Strong understanding of CAG (Cache-Augmented Generation), RAG variants (naive, hybrid, GraphRAG, agentic RAG), and the trade-offs between each.
 * Experience integrating with enterprise systems: REST/GraphQL APIs, event streams (Kafka, EventBridge), identity (OIDC, SAML, OAuth2), and enterprise data platforms (Snowflake, Databricks, Fabric, BigQuery).
 * Excellent written and verbal communication; able to move fluidly between executive narrative and engineering whiteboard.
Education
Bachelor's degree in Computer Science, Engineering, Mathematics, or a related technical field, or equivalent demonstrable experience. Advanced degree is welcomed but not required.

## Description

We are seeking a Senior Technical Solutions Architect - AI to serve as a hands-on, platform-agnostic technical architect for our strategic AI engagements. This person sits at the intersection of customer strategy, applied AI engineering, and modern software delivery. They translate ambiguous business problems into working prototypes, scalable reference architectures, and production-grade solutions across public-cloud hyperscaler AI platforms and sovereign (on-premise / private) AI environments.

The ideal candidate is equally comfortable whiteboarding an agentic architecture with a CIO, writing the proof-of-concept code that proves it works, and guiding a client engineering team through the secure path to production. They are vendor-fluent but vendor-neutral - recommending the right tool for the workload, the data, the risk profile, and the budget.

What You'll Do:

Solutioning & Architecture

 * Architect sovereign / on-premise AI solutions using stacks such as NVIDIA AI Enterprise (NIM, NeMo, Blueprints), Dell AI Factory, HPE Private Cloud AI, Red Hat OpenShift AI, Run:ai, and open-source model serving (vLLM, TGI, Ollama) - for clients with data residency, regulatory, IP, or air-gapped requirements.
 * Design end-to-end AI solutions spanning Generative AI (RAG, CAG, GraphRAG, fine-tuning, model distillation) and agentic AI (tool-using agents, multi-agent orchestration, MCP-based integrations).
 * Understanding of hybrid architecture across all major hyperscaler AI stacks - AWS (Bedrock, SageMaker, Q), Microsoft Azure (Azure AI Foundry, Azure OpenAI), and Google Cloud (Vertex AI, Gemini)
 * Develop reusable reference architectures, decision frameworks, and trade-off analyses (cost, latency, accuracy, governance, sovereignty) that scale across the practice.
Client Engagement & Enablement

 * Serve as the senior technical voice in client conversations - from executive briefings through deep technical design sessions.
 * Partner with sales, delivery, and practice leadership to scope statements of work, estimate effort, and de-risk delivery.
 * Mentor architects, engineers, and consultants across the broader AI practice; raise the technical bar through code reviews, internal enablement, and reusable assets.
 * Stay ahead of the field - evaluate emerging models, frameworks, and protocols (e.g., MCP, A2A, ACP, new agent frameworks, new sovereign AI stacks) and bring well-reasoned points of view back to the practice.