Senior AI Solutions Architect

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Pear Tree.

WebsiteLinkedInAll Job Openings

1 - 10 employees

🎯 Recruiter

👥 HR Tech

🤝 B2B

Recruitment • HR Tech • B2B

Pear Tree is a Melbourne‑born offshore recruitment and hiring services company that connects Australian, US, and UK businesses directly with skilled offshore professionals (primarily from the Philippines) without agency markups. They provide end-to-end hiring support — sourcing, screening, skills assessments, video interviews, reference checks — and offer payroll, Employer‑of‑Record, and compliance assistance plus a six‑month replacement guarantee. Pear Tree recruits across IT, sales & marketing, finance, creative, admin and operations roles, emphasizing cost savings, direct pay to talent, and secure onboarding workflows.

📋 Description

• Understand and contribute to the Ellavait CRM and BI product architecture. • Work with Micado leadership to shape the technical roadmap. • Translate product requirements into secure, scalable, maintainable technical solutions. • Use AI coding tools such as Claude Code, Cursor, GitHub Copilot, Codex, Replit, or similar to accelerate delivery. • Personally contribute to development where required, especially in the early stages. • Review AI-generated code critically before it reaches production. • Protect the integrity of Ellavait’s multi-tenant architecture, including tenant isolation, role based access control, data security, auditability, and maintainability. • Help define coding standards, architecture decision records, delivery patterns, and AI development workflows. • Support sprint planning by breaking larger initiatives into technically clear work packages. • Help interview and assess future AI developers and QA automation resources. • Act as a trusted technical counterpart to our internal leadership team.

🎯 Requirements

• Senior full-stack software engineering background. • Strong relational database design and data modelling experience, including complex entity relationships, lookup relationships, junction objects, field types, referential integrity, indexing, and query performance. • Strong understanding of CRM-style data models, including accounts, contacts, leads, opportunities, activities, tasks, notes, emails, custom objects, and related records. • Ability to design metadata-driven data structures where customers can configure objects, fields, layouts, relationships, and workflows without custom code. • Strong understanding of tenant-scoped data access and the risks of data leakage in a multi tenant SaaS platform. • Strong understanding of AI and LLM application architecture, not only AI-assisted coding. • Experience or strong working knowledge of agentic workflows, tool calling, retrieval augmented generation, vector databases, embeddings, structured outputs, and AI guardrails. • Ability to design AI features that can safely reason over CRM records, relationships, documents, emails, tasks, and business workflows. • Ability to design human-in-the-loop controls, audit logs, permission checks, and approval flows for AI-driven actions. • Preferred technical experience • React and TypeScript. • Python and FastAPI. • PostgreSQL or other advanced relational databases. • Advanced SQL, query optimisation, indexing, joins, and database performance tuning. • Relational database design, including one-to-many, many-to-many, lookup relationships, junction objects, referential integrity, and schema migrations. • Data modelling for CRM, ERP, workflow, BI, or metadata-driven applications. • Metadata-driven SaaS architecture where objects, fields, layouts, relationships, permissions, and workflows can be configured dynamically. • Multi-tenant SaaS platforms, including tenant isolation, tenant-scoped queries, shared versus dedicated data models, and data leakage prevention. • Authentication and authorisation frameworks. • Role-based access control and permission-aware application design. • API architecture and integration design. • Data pipelines and integration architecture. • Event-driven architecture and background job processing. • Automated testing, CI/CD, and release governance. • Cloud deployment experience. • Vector databases or vector search, such as pgvector, Pinecone, Weaviate, Qdrant, or similar. • Embedding models and semantic search. • LLM orchestration frameworks or patterns. • AI agent frameworks, tool/function calling, and workflow automation. • Retrieval-augmented generation architecture. • Secure AI application design, including tenant-aware retrieval, permission checks, auditability, human-in-the-loop controls, and prevention of cross-tenant data exposure.