AI Solution Architect

Line of Service

Industry/Sector

Specialism

Management Level

Job Description & Summary

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 practices 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 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 Anthropics 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)

Certifications (if blank certifications not specified)

Required Skills

Optional Skills

Desired Languages (If blank desired languages not specified)

Travel Requirements

Available for Work Visa Sponsorship

Government Clearance Required

Job Posting End Date