AI Architect

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🌐 United Kingdom, Switzerland, +6 more countries – Remote

🇬🇧 United Kingdom – Remote 🇨🇭 Switzerland – Remote 🇺🇦 Ukraine – Remote 🇲🇩 Moldova – Remote 🇬🇪 Georgia – Remote 🇦🇲 Armenia – Remote 🇲🇪 Montenegro – Remote 🇹🇷 Turkey – Remote

⏰ Full Time

🟠 Senior

🔴 Lead

🤖 AI Engineer

👻 Ghost score 15%

What it means: the chance that no one is really hiring for this job.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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WhiteTech

WebsiteLinkedInAll Job Openings

51 - 200 employees

💳 Fintech

☁️ SaaS

🤝 B2B

Fintech • SaaS • B2B

WhiteTech is a product company that builds modular, white‑label fintech infrastructure and payment orchestration platforms for regulated financial businesses, including PSPs, neobanks, and Open Banking providers. The platform offers a payment orchestrator that routes payments across hundreds of methods, 500+ integrations, 50+ modular components exposed via a single API, and advanced features such as dynamic open banking routing, payment receipt verification, automated workflows (settlement, reconciliation), analytics, and compliance-ready security (PCI DSS, PSD2, DORA). WhiteTech positions itself as a fast‑to‑market, enterprise B2B fintech SaaS provider and technical services partner (it does not operate transactional accounts itself).

📋 Description

• Define and evolve WhiteTech’s AI architecture and long-term AI technology strategy • Design production-grade AI and LLM-powered solutions integrated with existing fintech products and services • Architect AI agents, conversational systems, intelligent workflows, and AI-assisted automation • Design Retrieval-Augmented Generation (RAG) architectures, enterprise knowledge systems, and semantic search solutions • Select AI models, model providers, platforms, and infrastructure based on business, technical, security, latency, and cost requirements • Define patterns for integrating LLMs and AI services into backend and microservice architectures • Design scalable orchestration layers for prompts, tools, agents, APIs, models, and enterprise data sources • Establish standards for prompt management, model routing, structured outputs, context management, and AI service integration • Define AI evaluation frameworks, quality metrics, monitoring, observability, and feedback loops • Design safeguards and guardrails addressing reliability, security, privacy, explainability, and responsible AI use • Address AI-specific risks including hallucinations, prompt injection, data leakage, model misuse, and unpredictable model behavior • Work with Security and Compliance teams to meet fintech data protection and regulatory requirements • Design model lifecycle management, versioning, experimentation, evaluation, and deployment strategies • Optimize AI systems for performance, latency, scalability, reliability, and cost • Evaluate emerging AI technologies for practical value in WhiteTech products and operations • Lead architectural reviews for AI initiatives and validate engineering-team solutions • Support Proof-of-Concept initiatives and move successful AI experiments into production • Collaborate with Product teams to identify and prioritize AI opportunities with measurable business impact • Create architectural documentation, reference architectures, technical guidelines, and reusable AI patterns • Mentor engineers and technical leaders on AI architecture and engineering best practices

🎯 Requirements

• 8+ years of software engineering experience, including significant architecture or technical leadership responsibility • Previous experience as AI Architect, Software Architect, Solution Architect, Principal Engineer, ML Architect, AI Engineer, or a similar senior technical role • Strong practical experience designing and delivering production-grade AI or LLM-powered systems • Deep understanding of modern Generative AI and Large Language Model architectures • Experience integrating commercial and/or open-source LLMs into production applications • Strong understanding of Retrieval-Augmented Generation (RAG), embeddings, vector search, and semantic retrieval • Experience designing AI agents, tool-calling workflows, or multi-step AI orchestration • Understanding of prompt engineering, context engineering, structured outputs, and model evaluation • Experience with AI/LLM APIs and model platforms such as OpenAI, Azure OpenAI, Anthropic, Google Gemini, AWS Bedrock, or similar technologies • Experience with vector databases or search technologies such as pgvector, Pinecone, Weaviate, Qdrant, Elasticsearch, or similar solutions • Strong understanding of distributed systems, microservices, APIs, asynchronous processing, and event-driven architecture • Strong backend engineering background and ability to integrate AI capabilities into complex existing systems • Experience with cloud-native environments, Docker, Kubernetes, and modern infrastructure • Understanding of AI observability, monitoring, tracing, evaluation, and production incident analysis • Strong understanding of security, privacy, access control, and data governance in AI systems • Experience designing systems that work with sensitive or regulated data • Ability to evaluate trade-offs between model quality, latency, reliability, privacy, and cost • Experience documenting and presenting architectural decisions to both technical and non-technical stakeholders • Ability to balance experimentation and innovation with production reliability and business priorities

🏖️ Benefits

• Competitive compensation package (commensurate with your experience) • 19 business days of flexible Paid Time Off (PTO) • Fully remote work with flexible hours (within the CET time zone) from any location of your choice • Values-driven culture with a friendly and supportive work environment • Opportunity to regularly participate in conferences and community events as both a participant and contributor • Matrix organizational structure designed to streamline communication, remove barriers, and promote efficiency

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