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
.
Key Responsibiliti
es Architecture & Technical Strate
gy Define 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
hip Architect scalable, production-grade solutions on Google Cloud Platform (GCP) and/or Microsoft Az ure, 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
ance Evaluate, 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
ship Partner 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
ions Minimum 13+ y ears 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 s cale, from concept through to production and operati
ons. Deep, hands-on expertise designing solutions on Google Cloud Platform (GCP) and/or Microsoft A zure (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 fundamen tals 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 certificat ions (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 engine ering — LLM selection and fine-tuning, prompt engineering, Retrieval-Augmented Generation (RAG), and evaluation strategies for non-deterministic sys
tems. Vector databases and retr ieval (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 frame works such as Py Torch and/or Tensor Flow, and core NLP techni
ques. Data architecture and engine ering — pipelines, distributed processing, feature stores, and data quality at s
cale. AI sec urity — defending against prompt injection, data leakage, adversarial attacks, and model poiso
ning. AI governance and responsib le AI — bias auditing, explainability, regulatory compliance, and human-in-the-loop de
sign. Enterprise integration and systems thi nking — integrating AI into existing platforms, APIs, and broader enterprise architecture, including awareness of adjacent and emerging technolo
gies.