AI Architect

Role Summary

We are seeking a seasoned AI Architect to define and own the technical architecture for our AI and Generative AI capabilities. Reporting to the Capability Lead, this is a senior, hands-on technical authority role responsible for shaping how AI solutions are designed, built, secured, and scaled across the organization

The AI Architect sets the technical direction and reference architectures that delivery teams build against, evaluates and selects technologies and platforms, and ensures solutions are robust, cost-effective, secure, and aligned with business strategy. The role combines deep technical expertise with the judgment to make architectural decisions that hold up over time, and the influence to guide engineers and team leads toward sound outcomes

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Key Responsibiliti

esArchitecture & Technical Strate

• gyDefine and own end-to-end architecture for AI, ML, and Generative AI solutions, from data and model layers through to deployment, integration, and monitorin
• g.Establish reference architectures, design patterns, and technical standards that delivery teams build agains
• t.Translate business and capability strategy into a coherent, forward-looking technical roadma
• p.Make and document key architectural decisions, including trade-offs around cost, performance, scalability, security, and vendor lock-i

n.
Solution Design & Technical Leaders

• hipArchitect scalable, production-grade solutions on Google Cloud Platform (GCP) and/or Microsoft Azure, selecting appropriate managed AI, data, and compute servic
• es.Provide hands-on technical leadership on complex or high-risk initiatives, including proofs of concept and critical design proble
• ms.Review designs and solutions for architectural soundness, scalability, security, and maintainabili
• ty.Ensure non-functional requirements — reliability, performance, cost, observability — are designed in from the sta

rt.
Technology Evaluation, Standards & Govern

• anceEvaluate, select, and rationalize AI/ML platforms, tooling, frameworks, and emerging technolog
• ies.Define and champion AI governance, responsible AI, and security standards across the lifecy
• cle.Establish best practices for model development, evaluation, deployment, and ongoing operations (MLOps / LLMO
• ps).Balance innovation with pragmatism, managing technical debt and long-term sustainabil

ity.Collaboration, Influence & Mentor

• shipPartner with the Capability Lead, business stakeholders, and enterprise architects to align AI architecture with the broader technology and business strat
• egy.Act as a trusted technical advisor to team leads and engineers, mentoring them and raising the technical bar across the t
• eam.Communicate complex architectural concepts and trade-offs clearly to both technical and executive audien
• ces.Contribute to capability building, reusable assets, and the organization's overall AI matur

ity.Required Qualificat

• ionsMinimum 13+ years of professional experience in software, data, or AI/ML, including significant experience in a solution or technical architecture r
• ole.Proven track record architecting and delivering production-grade AI/ML solutions at scale, from concept through to production and operati
• ons.Deep, hands-on expertise designing solutions on Google Cloud Platform (GCP) and/or Microsoft Azure (e.g., Vertex AI / Azure Machine Learning, managed data, compute, networking, and security servic
• es).Strong foundation in machine learning, deep learning, and Generative AI / LLMs, with the ability to make sound model and architecture choi
• ces.Strong software engineering fundamentals and proficiency in Python; solid command of data architecture and
• SQL.Demonstrated ability to set technical direction, define standards, and influence engineering teams without necessarily holding formal people-management author
• ity.Excellent communication, stakeholder-management, and decision-making ski
• lls.A bachelor's or master's degree in Computer Science, Engineering, or a related field, or equivalent practical experie

nce.Advantageous (Strongly Prefer

• red)Working knowledge of Amazon Web Services (AWS) and its AI/ML services — a clear advantage and valued for multi-cloud flexibil
• ity.Relevant cloud or architecture certifications (e.g., Google Professional ML Engineer / Cloud Architect, Azure Solutions Architect / AI Engine

er).
Good-to-Have Skills & Knowledge (Prefe

rred)Depth or strong working knowledge across several of these areas distinguishes a strong AI Archi

• tect:Generative AI engineering — LLM selection and fine-tuning, prompt engineering, Retrieval-Augmented Generation (RAG), and evaluation strategies for non-deterministic sys
• tems.Vector databases and retrieval (e.g., Pinecone, Weaviate, Milvus, pgvector) and grounding/knowledge architect
• ures.MLOps / LLMOps at scale — model serving, monitoring, CI/CD for ML, containerization (Docker), and orchestration (Kuberne
• tes).Deep learning frameworks such as PyTorch and/or TensorFlow, and core NLP techni
• ques.Data architecture and engineering — pipelines, distributed processing, feature stores, and data quality at s
• cale.AI security — defending against prompt injection, data leakage, adversarial attacks, and model poiso
• ning.AI governance and responsible AI — bias auditing, explainability, regulatory compliance, and human-in-the-loop de
• sign.Enterprise integration and systems thinking — integrating AI into existing platforms, APIs, and broader enterprise architecture, including awareness of adjacent and emerging technolo