AI Architect

We are looking for an AI Architect who will define and drive the architecture of AI-powered capabilities across WhiteTech’s fintech platform. You will be responsible for designing scalable, secure, reliable, and production-ready AI solutions, including LLM-powered applications, intelligent automation, AI agents, knowledge retrieval systems, and AI-enabled product features. This role combines strong software architecture expertise with hands-on understanding of modern AI technologies and their application in complex, regulated, high-load environments.

Your role and impact

As an AI Architect, you will shape WhiteTech’s AI technology strategy and define how artificial intelligence is integrated into our products, internal platforms, and engineering ecosystem. You will work closely with Engineering, Product, Security, Data, Compliance, and business stakeholders to identify high-impact AI use cases and transform them into sustainable technical solutions. You will act as a technical authority for AI architecture, establishing standards, patterns, and best practices while helping engineering teams safely and effectively adopt AI technologies.Your work will directly influence how WhiteTech uses AI to improve automation, operational efficiency, product capabilities, customer experience, and engineering productivity.

Your key responsibilities will include

Defining and evolving WhiteTech’s AI architecture and long-term AI technology strategy.

Designing production-grade AI and LLM-powered solutions integrated with existing fintech products and services.

Architecting AI agents, conversational systems, intelligent workflows, and AI-assisted automation.

Designing Retrieval-Augmented Generation (RAG) architectures, enterprise knowledge systems, and semantic search solutions.

Selecting appropriate AI models, model providers, platforms, and infrastructure based on business, technical, security, latency, and cost requirements.

Defining patterns for integrating LLMs and AI services into existing backend and microservice architectures.

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

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

Defining AI evaluation frameworks, quality metrics, monitoring, observability, and feedback loops.

Designing safeguards and guardrails to improve reliability, security, privacy, explainability, and responsible use of AI.

Addressing AI-specific risks including hallucinations, prompt injection, data leakage, model misuse, and unpredictable model behavior.

Working with Security and Compliance teams to ensure AI solutions meet fintech data protection and regulatory requirements.

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

Optimizing AI systems for performance, latency, scalability, reliability, and cost.

Evaluating emerging AI technologies and determining their practical value for WhiteTech products and operations.

Leading architectural reviews for AI initiatives and validating solutions proposed by engineering teams.

Supporting Proof-of-Concept initiatives and helping teams move successful AI experiments into production.

Collaborating with Product teams to identify and prioritize AI opportunities with measurable business impact.

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

Mentoring engineers and technical leaders on AI architecture and engineering best practices.

What makes you stand out

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 working 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.

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.

What success looks like in this role

WhiteTech has a clear and 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 architectural 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.

Our recruitment process typically follows these stages:

Application Screening

Interview Process

Prescreening Call with a Recruiter

Technical Interview

Final Interview

Job Offer & Background Check

Embrace the opportunity to develop your skills in a cutting-edge fintech environment.

Please apply now to be part of our dynamic team and make a tangible impact on the future of payments!

Let’s build something great together!