Senior Platform Engineer

Job Title

Required Technical Skills

• 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 / Type Script – full-stack capability for modern service development, API layers, and Node.js 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 / Git Lab)
• Database engineering – SQL Server, Oracle RDS, and modern alternatives (PostgreSQL, columnar stores such as Click House); 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
• Git Hub 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

Advantageous Skills

• Platform migration / replatforming – strangler fig pattern, parallel running; experience shipping large-scale migrations
• Event streaming – Kafka, Event Bridge as replacement strategies for complex Saga chains
• Redis, RabbitMQ / Mass Transit – understanding current patterns to inform migration strategy
• Click House 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, SQL Alchemy for data exploration and migration validation

AI-First Cognitive Requirements

• 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

Key Responsibilities

• Assess and decompose the existing 34-microservice architecture to identify simplification and replacement opportunities
• Design and build next-generation backend services using modern Python / Type Script 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

Knowledge Transfer & Key-Person Risk Mitigation

• Pair regularly with the existing DB specialist to transfer backend architecture and…