AI Architect

Key Responsibilities

• Assemble architecture designs — produce clear, end-to-end architecture artifacts: solution designs, reference architectures, diagrams, patterns, and architecture decision records.
• Apply AI capabilities — design and integrate AI / GenAI / machine-learning components into solutions, and advise on appropriate patterns, tooling, and trade-offs.
• Drive rapid prototypes — build quick proofs of concept and working prototypes to validate architecture decisions, de-risk options, and accelerate stakeholder alignment.
• Partner across teams — work with engineering, product, and business partners to align architecture with delivery goals and non-functional requirements (security, scalability, resilience, cost).
• Document and communicate — present designs and trade-offs clearly to both technical and non-technical audiences, and keep architecture documentation current and usable.
• Uphold standards — apply enterprise architecture standards, controls, and best practices throughout design and prototyping.

Required Qualifications (Must-Have)

• Demonstrable hands-on AI experience — practical work building, integrating, or architecting AI / GenAI / ML solutions.
• Proven ability to put architecture details together — a track record of producing clear, complete architecture designs and documentation.
• Ability to drive quick prototypes — rapidly stand up POCs and working prototypes to test and demonstrate ideas.
• Senior, hands-on technical background operating at a Lead Architect level, with strong design fundamentals across modern application, integration, and cloud patterns.
• Strong communication skills — able to explain architecture and trade-offs to technical teams and business stakeholders alike.
• Self-directed and comfortable working across ambiguity to deliver tangible outcomes quickly.

Technical Skills

Representative technical skills for these roles. Candidates should bring strong depth across several of these areas — tailor to our stack as needed:

• AI & Machine Learning — GenAI and large language models (LLMs), retrieval-augmented generation (RAG), agentic and prompt-engineering patterns, model APIs and integration, embeddings and vector stores; familiarity with common ML frameworks.
• Cloud & Platform — hands-on experience with at least one major cloud (AWS, Azure, or Google Cloud Platform); containers and orchestration (Docker, Kubernetes); serverless services.
• Architecture & Integration — microservices, event-driven and API-led design, REST / GraphQL APIs, messaging and streaming (e.g., Kafka), and enterprise integration patterns.
• Data & Information Architecture — data modeling, relational and NoSQL databases (strong SQL), data lakes / warehouses, ETL / ELT pipelines, and data governance / metadata.
• Languages & Prototyping — proficiency in Python and/or Java (or comparable); rapid prototyping, scripting, and notebook-based experimentation.
• Engineering Practices — CI/CD, infrastructure as code (e.g., Terraform), Git-based version control, and automated testing.
• Architecture Tooling — modeling and diagramming (C4, UML, or ArchiMate) and architecture decision records (ADRs).

Preferred Qualifications (Nice-to-Have)

The following are strong pluses and will differentiate candidates, but are not strict requirements:

• Depth in data / information architecture — experience designing data models, information flows, data platforms, or enterprise information architecture.
• Finance domain experience — prior work in Finance functions, especially Controllers / financial control and related processes.