AI Architect

This is a fully remote position, open to applicants in United Kingdom, +7 more countries.

📋 Description

• Define and enhance WhiteTech’s AI architecture along with its long-term AI technology strategy.

• Create production-ready AI and LLM-powered solutions that are integrated with existing fintech products and services.

• Architect AI agents, conversational systems, intelligent workflows, and AI-driven 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 parameters.

• Establish patterns for incorporating LLMs and AI services into backend and microservice architectures.

• Create scalable orchestration layers for prompts, tools, agents, APIs, models, and enterprise data sources.

• Set standards for prompt management, model routing, structured outputs, context management, and AI service integration.

• Define AI evaluation frameworks, quality metrics, monitoring, observability, and feedback mechanisms.

• Design safeguards and guardrails that address reliability, security, privacy, explainability, and responsible AI use.

• Tackle AI-specific risks such as hallucinations, prompt injection, data leakage, model misuse, and unpredictable model behavior.

• Collaborate with Security and Compliance teams to ensure adherence to fintech data protection and regulatory requirements.

• Develop strategies for model lifecycle management, versioning, experimentation, evaluation, and deployment.

• Optimize AI systems for performance, latency, scalability, reliability, and cost-effectiveness.

• Assess emerging AI technologies for their practical value in WhiteTech products and operations.

• Lead architectural reviews for AI initiatives and validate solutions proposed by the engineering team.

• Assist in Proof-of-Concept initiatives and transition successful AI experiments into production.

• Work alongside Product teams to identify and prioritize AI opportunities that yield measurable business impact.

• Generate architectural documentation, reference architectures, technical guidelines, and reusable AI patterns.

• Mentor engineers and technical leaders on best practices in AI architecture and engineering.

⛳️ Requirements

• A minimum of 8 years of software engineering experience, with substantial architecture or technical leadership responsibilities.

• Prior experience in roles such as AI Architect, Software Architect, Solution Architect, Principal Engineer, ML Architect, AI Engineer, or similar senior technical positions.

• Strong hands-on experience in designing and delivering production-grade AI or LLM-powered systems.

• In-depth knowledge of modern Generative AI and Large Language Model architectures.

• Experience in integrating commercial and/or open-source LLMs into production applications.

• Strong understanding of Retrieval-Augmented Generation (RAG), embeddings, vector search, and semantic retrieval methodologies.

• Experience in designing AI agents, tool-calling workflows, or multi-step AI orchestration.

• Familiarity with 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.

• Comprehensive understanding of distributed systems, microservices, APIs, asynchronous processing, and event-driven architecture.

• Strong backend engineering background with the ability to integrate AI capabilities into complex existing systems.

• Experience with cloud-native environments, Docker, Kubernetes, and contemporary infrastructure.

• Knowledge of AI observability, monitoring, tracing, evaluation, and production incident analysis.

• Solid understanding of security, privacy, access control, and data governance in AI systems.

• Experience designing systems that handle sensitive or regulated data.

• Capability to evaluate trade-offs between model quality, latency, reliability, privacy, and cost.

• Experience in 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 based on 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 fostering 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 enhance communication, eliminate barriers, and promote efficiency.