AI Architect

AI Architect

Location: Remote

WhiteTech is a remote-first technology company providing white-label and SaaS solutions for EMI, PSP, e-commerce, and open banking platforms.

WhiteTech welcomes applications from candidates across Europe, including the EEA, UK, Switzerland, Ukraine, Moldova, Georgia, Armenia, Montenegro, Turkey, and the Western Balkans. Applicants in other locations may also be considered if they can work within Central European Time (CET).

Role overview

WhiteTech is looking for an AI Architect to define and drive the architecture of AI-powered capabilities across its fintech platform. You will design scalable, secure, reliable, production-ready AI solutions, including LLM-powered applications, intelligent automation, AI agents, knowledge-retrieval systems, and AI-enabled product features.

The role combines strong software architecture expertise with hands-on knowledge of modern AI technologies and their use in complex, regulated, high-load environments. You will work closely with Engineering, Product, Security, Data, Compliance, and business stakeholders, serving as a technical authority for AI architecture and establishing standards, patterns, and best practices.

Key responsibilities

• 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, 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 for reliability, security, privacy, explainability, and responsible AI use.
• Address risks such as 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 strategies for model lifecycle management, versioning, experimentation, evaluation, and deployment.
• Optimize AI systems for performance, latency, scalability, reliability, and cost.
• Evaluate emerging AI technologies and their practical value for WhiteTech products and operations.
• Lead architectural reviews and validate solutions proposed by engineering teams.
• Support proof-of-concept initiatives and help move successful 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.

Qualifications

• 8+ years of software engineering experience, including significant architecture or technical leadership responsibility.
• Previous experience as an AI Architect, Software Architect, Solution Architect, Principal Engineer, ML Architect, AI Engineer, or similar senior technical professional.
• 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 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 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 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 among model quality, latency, reliability, privacy, and cost.
• Experience documenting and presenting architectural decisions to technical and non-technical stakeholders.
• Ability to balance experimentation and innovation with production reliability and business priorities.

Nice to have

• Experience in fintech, payments, banking, EMI, PSP, or open banking.
• Experience implementing AI solutions in regulated industries.
• Experience with AI-powered fraud detection, compliance, AML/KYC, risk management, customer support, or financial operations.
• Experience with agentic architectures and multi-agent systems.
• Experience with LLM evaluation frameworks and automated AI quality testing.
• Experience with fine-tuning, model customization, or self-hosted/open-source models.
• Understanding of MLOps and LLMOps practices.
• Experience building AI gateways, model abstraction layers, or multi-model architectures.
• Experience with knowledge graphs and advanced information retrieval.
• Experience implementing human-in-the-loop AI workflows.
• Experience with platform modernization and large-scale distributed systems.
• Experience defining technical standards across multiple engineering teams.
• Experience mentoring Senior and Lead Engineers.

Measures of success

• WhiteTech has a clear, scalable architecture for building and integrating AI-powered capabilities.
• Engineering teams follow consistent AI architecture, security, and engineering standards.
• AI prototypes can be efficiently transformed into reliable production solutions.
• AI-powered features deliver measurable business and customer value.
• AI systems are observable, testable, secure, cost-efficient, and maintainable.
• Model quality and AI application performance are continuously measured and improved.
• AI-specific security, privacy, compliance, and reliability risks are systematically addressed.
• Reusable AI components and patterns reduce development time across teams.
• WhiteTech can adopt new AI technologies without becoming tightly coupled to individual vendors or models.
• Architecture decisions enable rapid AI innovation without compromising the reliability, security, and scalability expected from a financial technology platform.

Compensation and benefits

• Competitive compensation commensurate with experience.
• 19 business days of flexible paid time off.
• Fully remote work with flexible hours within the CET time zone.
• Friendly, supportive, values-driven culture.
• Opportunities to participate in conferences and community events as a participant and contributor.
• Matrix organizational structure designed to streamline communication, remove barriers, and promote efficiency.

Recruitment process

1. Application screening
2. Prescreening call with a recruiter
3. Technical interview
4. Final interview
5. Job offer and background check

Apply to join WhiteTech and contribute to the future of payments.