AI Architect is a AI Architect role (full-time). with PwC Ireland. in 4 LOCATIONS, IE. Imported listing (source: thejobsmap.com). Apply on the employer's site (thejobsmap.com).
Imported listing (source: thejobsmap.com) · Apply on thejobsmap.com
The sections below reproduce the third-party job description for reference. AIEngineer.careers does not write or control this text.
Imported job description
Sourced from thejobsmap.com
PwC Ireland is seeking an AI Architect to design and guide production-ready Generative AI solutions for clients. This role combines hands-on architecture, technical leadership, and client collaboration to build secure, scalable AI systems. You will lead the architecture and delivery of Generative AI solutions, define evaluation and security strategies, and mentor engineers.
Responsibilities
Lead the architecture and delivery of Generative AI solutions, guiding development teams on tools, frameworks, and platforms.
Translate business requirements into robust application, service, and target-state architectures, including reusable RAG, agent orchestration, prompt, and evaluation patterns.
Own scalability, performance, latency, observability, security, and cost optimisation across AI solutions.
Lead technical design and code reviews and guide delivery against client needs and quality standards.
Define the right adaptation approach for each use case—prompting, RAG, fine-tuning, or distillation—including when Generative AI is not appropriate.
Own evaluation strategy, including golden datasets, offline and online evaluation, human-calibrated LLM-as-judge, and CI regression testing.
Set retrieval quality standards for chunking, hybrid search, reranking, query rewriting, and permission-aware retrieval, using measured recall and precision.
PwC Ireland is the Irish member firm of the PwC professional-services network. It helps organisations, government bodies, and private businesses through audit and assurance, tax, advisory, consulting, deals, and related business-transformation capabilities.
Define AI security, privacy, Responsible AI, and governance controls, including prompt injection, data exfiltration, jailbreak, sandboxing, least-privilege identity, and supply-chain risks.
Own solution economics through model routing, caching, batching, capacity planning, and defensible cost-per-transaction estimates.
Support enterprise architecture standards and help assess technology solutions.
Supervise vendor developers and mentor engineers and junior architects.
Support business development by identifying and researching opportunities with new and existing clients.
Improve internal design and development practices and maintain documentation.
Requirements
5+ years of experience in agile product delivery.
Hands-on experience designing and delivering production software solutions at scale.
Demonstrated expertise in Generative AI solution architecture.
Personally delivered at least one Generative AI system to production and can explain its architecture, failure modes, operating costs, and lessons learned.
Bachelor's or Master's degree in Computer Science or Engineering, or equivalent practical experience.
Strong hands-on experience with Generative AI architecture.
Experience with large language models such as GPT-4, Gemini, and Llama.
Experience with RAG, agent orchestration, and Microsoft Copilots using platforms or frameworks such as Azure OpenAI Service, Semantic Kernel, LangChain, AWS Bedrock, or Google Vertex AI.
Experience with vector search and retrieval engineering, including chunking, hybrid search, reranking, query rewriting, metadata filtering, and permission-aware retrieval.
Experience with production AI agents, including tool calls, state and memory management, durable execution, approval controls, retries, idempotency, and cost safeguards.
Experience with structured generation, including schema-enforced outputs, validation and repair, deterministic fallbacks, and graceful degradation.
Cloud-native Azure architecture experience, including Azure Machine Learning, Azure AI services, and appropriate infrastructure and platform services.
Experience with service-oriented, event-driven, and microservices architectures.
Experience with relational and NoSQL data stores, such as Microsoft SQL Server and MongoDB.
Experience with containers and orchestration using Docker and/or Kubernetes.
DevOps and delivery practices, including CI/CD, automation, environment management, and tools such as Azure DevOps, GitHub, or Jira.
Working knowledge of user-centred product practices, including user stories, personas, and prototyping.
AI evaluation using golden datasets, LLM-as-judge, and prompt and model regression testing.
LLMOps and observability, including tracing, token and cost attribution, prompt and model versioning, trace replay, and quality-drift monitoring, using tools such as LangSmith, Langfuse, or Azure Monitor.
Model economics, including routing, cascading, caching, batching, and provisioned-versus-consumption capacity planning.
AI security, including prompt injection, data exfiltration, jailbreak resistance, sandboxing, least-privilege identities, supply-chain risk, and the OWASP Top 10 for LLM Applications.
Fluent written and spoken English.
Clear written and verbal communication with product teams, clients, and stakeholders.
Strong collaboration and client focus.
Nice to Have
Model Context Protocol and agent interoperability standards.
Model adaptation beyond prompting and RAG, including fine-tuning, LoRA/PEFT, and distillation.
Multimodal AI, including document intelligence, vision, speech, or voice agents.
AI regulation and assurance frameworks, including the EU AI Act, ISO/IEC 42001, and NIST AI RMF.
Consulting or professional-services experience with external clients.
Relevant Microsoft Azure certification, such as Azure Solutions Architect Expert or Azure Developer Associate.
Benefits
Health insurancemeal voucherssport cardannual bonusremote workprofessional development
