AI Solutions Architect

Role Overview

SHIZA Developer’s drag-and-drop builder lets organisations ship sophisticated, agent-powered automations in days not months. As our AI Automation Solutions Consultant you will:

• Scope client requirements, run discovery workshops, and convert ideas into architecture diagrams, Proposals, Statements of Work etc.
• Prototype & launch production-ready AI workflows/agents using a combination of SHIZA Developer plus other open-source and proprietary third-party commercially available tools and frameworks (think Eleven Labs, Cartesia, Deepgram, Pinecone, LangChain, LangGraph and many more)
• Orchestrate no-/low-code integrations (n8n, Zapier, Make, Flowise) that connect AI with mainstream SaaS stacks.
• Document & transfer knowledge so clients and internal teams can maintain what you build.
• Research & innovate stay on top of the SOTA agent-framework landscape (CrewAI, Semantic Kernel, AWS Bedrock, Vertex AI, Azure OpenAI, Claude 3.5, Gemini 2) and feed lessons back into our product strategy.

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Key Responsibilities

1. Solution Scoping & Architecture

• Conduct discovery calls and capture technical/business requirements
• Design system architectures and data-flow diagrams using Lucidchart, Draw.io, Napkin.ai, Mermaidchart.live
• Prepare Statements of Work (SoW), effort estimates, and cost breakdowns

2. Workflow & Agent Development

• Build, test, and deploy AI workflows and agents using SHIZA Developer, LangChain, LangGraph, or AutoGen
• Integrate vector databases such as Pinecone and Weaviate
• Connect with LLM endpoints (OpenAI, Claude, Gemini, Ollama/open-source models)

3. Low-/No-Code Integrations

• Develop end-to-end automations using Flowise, n8n, Zapier, and Make
• Utilize SaaS connectors for tools like Slack, HubSpot, Airtable, Google Workspace, and Notion

4. Documentation & Enablement

• Create user guides, API hand-offs, demo videos, and run client workshops
• Ensure seamless knowledge transfer and project continuity

5. Innovation & Tool Research

• Explore and evaluate emerging agent frameworks (e.g., CrewAI, Semantic Kernel)
• Assess cloud LLM offerings such as AWS Bedrock, Vertex AI, and Azure OpenAI
• Recommend suitable technologies and adoption paths

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Required Skills & Experience

• 3 – 5 years delivering AI/ML, automation or software-consulting projects end-to-end, owning client outcomes
• Client-facing communication clear written proposals & confident workshop facilitation in English (Great command of spoken and written English is mandatory)
• Hands-on with modern agent & RAG frameworks: LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI
• Vector databases & embeddings: Pinecone, Chroma, Weaviate; able to tune retrieval for latency & relevance
• Low-/ No-Code ecosystems: SHIZA Developer, Flowise, Bubble, Retool, Zapier, Make/Integromat, n8n
• Cloud & DevOps expertise (one major platform required, multi-cloud a plus)
  • AWS
  • GCP
  • Azure
  • Optional:
  • IaC (Terraform / Pulumi), CI/CD (GitHub Actions / GitLab CI), Docker & Kubernetes basics
• API & webhook literacy OAuth2 / JWT, REST/GraphQL, JSON handling, Postman, Insomnia
• Proficient in Python and JavaScript/TypeScript for custom nodes, SDK wrappers, data-processing snippets
• Security & compliance mindset RBAC, secrets management, OWASP Top 10, GDPR basics
• Solution-architecture artefacts diagrams, RACI matrices, cost models, runbooks

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AI Tooling Mastery – Absolutely Essential

Daily use of cutting-edge AI productivity tools across coding, documentation and research, e.g.:

• Coding assistants: GitHub Copilot / Copilot Workspace, Amazon Q Developer / CodeWhisperer, Replit Agent, Cursor IDE
• Function-calling & agentic LLMs: OpenAI /Anthropic / Assistants API, Claude 3.5, Gemini Code Assist, Google Stitch
• AI UI/UX generators: v0.dev, Lovable AI for rapid front-end scaffolding
• Content & research accelerators: ChatGPT, Perplexity, Notion AI, Firefly / Midjourney / DALL·E for visuals

You weave these tools into every part of the job from code and unit tests to diagrams, SoWs, slide decks and competitive analyses.

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Prompt Engineering

You instinctively deconstruct problems into the right roles and messages, crafting clear system, user, and tool prompts that guide LLMs to deterministic outputs; you iterate with techniques like chain-of-thought, few-shot, and retrieval-augmented generation, then validate them through automated prompt-evaluation harnesses. Your toolkit includes guard-rails (JSON schema, function calling), prompt versioning, and cost/latency trade-off analysis, enabling you to ship robust, production-grade prompts that remain resilient as models evolve.

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Nice-to-Have

• Experience with multi-tenant SaaS design, RBAC and Kubernetes
• Prior consulting in an agency/SI setting juggling multiple concurrent client projects
• MLOps or model-ops exposure: fine-tuning, evaluation harnesses, model-drift monitoring
• Knowledge of RAG optimisation, prompt-engineering patterns, evaluation frameworks (LM-Eval, PromptFlow)
• Certifications: AWS Solutions Architect, Google PCA, Azure AI Engineer, or equivalen

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What Success Looks Like (First 6 Months)

• Deliver 2 – 3 production-grade AI workflows for external clients, fully documented and handed off
• Publish a reusable SHIZA Developer node/plugin adopted internally by ≥ 50 % of new projects
• Codify our discovery → design → delivery playbook into repeatable templates & checklists
• Mentor junior builders and evangelise low-code AI best practices across the organisation

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Compensation & Logistics

• Competitive base + performance bonus
• Remote-first team
• Generous PTO, local holidays