Line of Service

Assurance

Industry/Sector

Not Applicable

Specialism

Risk

Management Level

Manager

Job Description & Summary

At PwC, we help clients build trust and reinvent so they can turn complexity into competitive advantage. We're a tech-forward, people-empowered network with more than 364,000 people in 136 countries and 137 territories. Across audit and assurance, tax and legal, deals and consulting, we help clients build, accelerate, and sustain momentum. Find out more at www.pwc.com.

Core Responsibilities

Lead technical solutioning in client pre-sales and discovery across all sectors — translating business problems into AI architectures (RAG pipelines, agentic workflows, SDLC automation, data platforms, model risk frameworks)
Own the technical sections of client proposals and engagement scoping documents, including architecture diagrams and implementation sequencing
Build and maintain reusable accelerators and demo assets deployable within 48 hours for client workshops across all use cases
Lead or co-lead technical delivery on AI pilot engagements from architecture through to production handover
Stay current on the AI tooling landscape — with particular depth in the Anthropic/Claude ecosystem — and translate into client-relevant recommendations
Advise on AI governance and responsible AI design, particularly for FS clients subject to MAS regulatory scrutiny on model risk, explainability, and audit trails

Must-Have Skills

Python proficiency — LLM integration, API development, data engineering, and automation scripting
Cloud AI platforms — Azure OpenAI Service, AWS Bedrock, or GCP Vertex AI (at least one in depth)
LLM orchestration — LangChain, LlamaIndex, or equivalent; multi-agent frameworks (CrewAI, AutoGen, or similar)
Vector databases — Pinecone, Weaviate, Chroma, pgvector, or equivalent
Containerisation and CI/CD — Docker, basic Kubernetes, GitHub Actions
5-14 years enterprise technology experience; minimum 2 years in production AI delivery

Anthropic / Claude Ecosystem — Strongly Preferred

Given the practice's primary AI platform orientation, depth in the Anthropic/Claude ecosystem is a material differentiator. Candidates with hands-on production experience across multiple Claude capabilities will be prioritised.

Claude API — tool use, computer use, vision, and document processing in production applications
Claude Code — agentic coding workflows, CLI integration, MCP server configuration, and multi-agent software development pipelines
Claude claude.ai and Projects — enterprise deployment patterns, system prompt design, memory and context management
Anthropic prompt engineering — chain-of-thought elicitation, XML-structured outputs, multi-turn conversation design, and retrieval-augmented prompting
Claude model family knowledge — Opus, Sonnet, Haiku trade-offs for latency, cost, and capability in production architectures
MCP (Model Context Protocol) — server implementation, tool registration, and integration with enterprise data sources (Google Drive, Gmail, Slack, CRMs)
Anthropic API batch processing, streaming, and rate limit management for enterprise-scale deployments
AI safety and responsible AI design aligned with Anthropic's principles

Broader AI Tooling — Preferred

Local LLM deployment — Ollama, Qwen, Mistral, Llama on Apple Silicon or equivalent edge hardware for air-gapped or data-sovereign deployments
GitHub Copilot, Cursor, or equivalent AI-assisted development environments — production use in SDLC automation contexts
Open-source agent frameworks — LangGraph, AutoGen, CrewAI, or equivalent for multi-agent orchestration
SDLC and DevTest automation — AI-assisted test generation, code review pipelines, and CI/CD integration
Security and compliance design — data residency, air-gapped deployment patterns, PDPA and MAS regulatory considerations for Singapore deployments
Front-end familiarity — React or equivalent for building lightweight internal tools and executive dashboards

Education (if blank, degree and/or field of study not specified)

Degrees/Field Of Study Required

Degrees/Field of Study preferred:

Certifications (if blank, certifications not specified)

Required Skills

Optional Skills

Accepting Feedback, Accepting Feedback, Active Listening, AI Implementation, Analytical Thinking, C++ Programming Language, Coaching and Feedback, Communication, Complex Data Analysis, Creativity, Data Analysis, Data Infrastructure, Data Integration, Data Modeling, Data Pipeline, Data Quality, Deep Learning, Embracing Change, Emotional Regulation, Empathy, GPU Programming, Inclusion, Intellectual Curiosity, Java (Programming Language), Learning Agility + 30 more

Desired Languages (If blank, desired languages not specified)

Travel Requirements

Not Specified

Available for Work Visa Sponsorship

Yes

Government Clearance Required

No

Job Posting End Date

AI Solution Architect