AI Solution Architect

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  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)

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  Own the technical sections of client proposals and engagement scoping documents, including architecture diagrams and implementation sequencing

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  Build and maintain reusable accelerators and demo assets deployable within 48 hours for client workshops across all use cases

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  Lead or co-lead technical delivery on AI pilot engagements from architecture through to production handover

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  Stay current on the AI tooling landscape — with particular depth in the Anthropic/Claude ecosystem — and translate into client-relevant recommendations

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  Advise on AI governance and responsible AI design, particularly for FS clients subject to MAS regulatory scrutiny on model risk, explainability, and audit trails

• 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

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

• 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