Senior Platform Engineer

Job Description:

• C# / .NET Core 8 deep understanding of the existing backend; required to assess refactor and migrate to modern architecture

• Python modern backend development AI/ML integration data pipelines automation scripting and rapid prototyping of replacement services; required across all three roles

• JavaScript / TypeScript full-stack capability for modern service development API layers and tooling

• JSON schema design API contracts configuration-as-code LLM function calling specifications structured data interchange

• Markdown documentation-as-code: ADRs AI constitutions specification documents runbooks

• Docker / Kubernetes (EKS) containerised deployment and orchestration; Helm charts and CI/CD pipelines (Jenkins / GitLab)

• Database engineering SQL Server Oracle RDS and modern alternatives (PostgreSQL columnar stores such as ClickHouse); stored procedures query optimisation and schema migration

• Data modelling & analysis designing data schemas for the replacement platform; understanding costing WBS trees commodities elements and financial factors; data quality frameworks and analytical pipelines

• GitHub Copilot and Claude Code AI-first development as the default working mode not an optional add-on

• LLM integration using AI models to replace rigid business logic with intelligent adaptable solutions

• API-first design RESTful GraphQL and event-driven patterns for loosely-coupled architectures

• Platform migration / replatforming strangler fig pattern parallel running; experience shipping large-scale migrations

• Event streaming Kafka EventBridge as replacement strategies for complex Saga chains

• Redis RabbitMQ / MassTransit understanding current patterns to inform migration strategy

• ClickHouse or modern analytics alternatives columnar analytics for costing and reporting data

• Frontend frameworks: Angular 2 React / Redux (awareness level is sufficient)

• Python data analysis libraries pandas SQLAlchemy for data exploration and migration validation

• Evaluative judgment ability to distinguish plausible AI-generated code from correct code; in high-stakes pricing logic never self-certify money through AI alone

• Specification precision ability to articulate precise intent edge cases and constraints before AI generates code; quality of specification determines everything downstream

• Collaborative scepticism working productively with AI as a collaborator you direct and challenge not a tool you wield or an oracle you trust

• Constitution-building mindset encoding failures as permanent constraints; maintaining Architecture Decision Records (ADRs) that capture why decisions were made not just what was decided

• Data-first verification the instinct that AI is only as good as the data it works from; verifying data quality at every system boundary before trusting AI outputs

• Assess and decompose the existing 34-microservice architecture to identify simplification and replacement opportunities

• Design and build next-generation backend services using modern Python / TypeScript stacks with LLM-augmented business logic

• Own database architecture redesign migrating from Oracle RDS / MSSQL to modern schemas (PostgreSQL event stores) that are AI-queryable and analytics-ready

• Build data quality frameworks and validation pipelines that ensure AI systems work from reliable well-structured data

• Replace rigid Saga / Orchestrator chains with AI-driven workflow engines and simpler event-driven patterns

• Develop LLM-powered solutions to replace hard-coded costing rules financial calculations and allocation logic

• Operate in the Intent Generate Verify Decide Document loop owning decisions on irreversible money-touching or outward-facing logic

• Build migration pathways (strangler fig parallel running) to transition from legacy to modern architecture without service disruption

• Performance tuning and observability using Grafana Kibana and the ELK stack

• Pair regularly with the existing DB specialist to transfer backend architecture and data modelling knowledge; document all schema decisions in Markdown-based ADRs

• Run fortnightly architecture clinics with the wider team covering Onion Architecture DDD patterns and database design for the new platform

• Maintain living runbooks so that every critical backend process can be operated or debugged by at least one other team member within 60 days of joining

• Contribute to -style AI constitutions encoding pricing logic constraints and data quality rules ensuring institutional knowledge lives in the system not just in heads

• Cross-train at least one frontend developer on backend API design and Python data pipelines within the first 6 months

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