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EY

AI Architect – Tech Consulting

EY

. Define end-to-end enterprise AI architectures aligned with business strategy, enterprise architecture, and technology innovation .

Posted 11/4/2026full-timeEbene • MauritiusMid-LevelSeniorWebsite

Core Competencies

Role fit
Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in designing end-to-end enterprise AI architectures that align with business strategy and technology innovation, with a strong focus on Generative AI, LLMs, and MLOps. Proven ability to translate business requirements into scalable, secure AI solutions and reusable architecture patterns.

Highest-signal resume keywords
AI Architecture DesignGenerative AI ExpertiseMLOps/LLMOps UnderstandingCloud Architecture ExperienceAPI Integration Skills

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills
Generative AILLMsMachine LearningData EngineeringSQLNoSQLDockerKubernetesCloud PlatformsAI Security
Soft Skills
CollaborationCommunicationConsultative ApproachProblem-SolvingStakeholder Engagement
Tools & Technologies
AWSAzureGCPVector DatabasesMulti-Agent Orchestration
Industry Keywords
Enterprise ArchitectureResponsible AIAI LifecycleArchitecture PatternsObservability

Tech Stack

Tools & technologies
AWSAzureCloudDockerGoogle Cloud PlatformKubernetesNoSQLReactSQL

About the role

Key responsibilities & impact
  • Define end-to-end enterprise AI architectures aligned with business strategy, enterprise architecture, and technology innovation
  • Design AI solution architectures spanning Generative AI, LLMs, Agentic AI, RAG, Machine Learning, Data Engineering, and cloud platforms
  • Translate business requirements into architecture blueprints and reusable patterns covering APIs, integration, data flows, and supporting infrastructure
  • Guide AI initiatives from proof of concept through production and scale across the AI/LLM lifecycle
  • Design scalable, resilient, and secure AI architectures, including APIs, integration, and supporting cloud infrastructure
  • Define architectural requirements for MLOps/LLMOps, observability, evaluation, security, and Responsible AI
  • Collaborate with business stakeholders, enterprise architects, and cross-functional teams to align architecture decisions with business needs and guide solution delivery
  • Perform other cognate duties
  • Connect business strategy with enterprise architecture and technology innovation in a consultative client-facing role
  • Communicate architectural decisions, guide cross-functional teams, challenge the status quo, and define reusable architecture patterns

Requirements

What you’ll need
  • Related bachelor's degree in Computer Science, Information Technology, or a related field, or any STEM degree
  • Proven experience as an AI Architect designing enterprise-grade AI solutions over the last five years
  • Strong expertise in Generative AI, LLMs, Agentic AI, RAG, Machine Learning, Data Engineering, and cloud platforms
  • Proven track record taking AI initiatives from proof of concept to production and scale
  • Strong understanding of the AI/LLM lifecycle, MLOps/LLMOps, observability, security, and Responsible AI
  • Experience designing scalable, resilient, and secure AI architectures, including APIs, integration, and supporting infrastructure
  • Ability to translate business requirements into end-to-end AI architectures and reusable architecture patterns
  • Ability to bridge business strategy, enterprise architecture, and technology innovation
  • Experience defining reusable AI architecture patterns for enterprise-wide adoption
  • Experience assessing AI technology choices and architectural trade-offs
  • Practical understanding of agent architectures, including ReAct, plan-and-execute patterns, tool calling, MCP, context and memory management, and agent evaluation
  • Experience aligning AI architecture standards with enterprise governance, security, and Responsible AI requirements
  • Understanding of LLMs, embeddings, fine-tuning versus RAG, and evaluation approaches
  • Knowledge of SQL/NoSQL and vector database architectures
  • Experience with AWS, Azure, or GCP cloud architecture
  • Experience with Docker and Kubernetes containerisation and orchestration
  • Multi-agent orchestration and inter-agent communication patterns, including A2A and agent swarms
  • Exposure to model deployment and open-source model fine-tuning patterns, including LoRA/QLoRA
  • Understanding of AI security concerns, including prompt injection, data leakage, and agent guardrails
  • Contributions to reusable AI architecture patterns or open-source AI/agent architecture initiatives

Benefits

Comp & perks
  • Competitive remuneration package
  • Rewards for individual and team performance
  • Flexible working arrangements
  • Career development support
  • Support, coaching, and feedback from colleagues
  • Opportunities to develop new skills and progress your career
  • Freedom and flexibility to handle the role in a way that’s right for you
  • Inclusive employer and flexible working arrangements
  • Client-facing professionals may receive travel opportunities