Job Description

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 / TypeScript – 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 / 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

Advantageous Skills

- 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

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 / 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
- Knowledge Transfer & Key-Person Risk Mitigation
- 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 CLAUDE.md-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

At DXC Technology, we believe strong connections and community are key to our success. Our work model prioritizes in-person collaboration while offering flexibility to support wellbeing, productivity, individual work styles, and life circumstances. We’re committed to fostering an inclusive environment where everyone can thrive.

DXC Technology (NYSE: DXC) is a leading enterprise technology and innovation partner delivering software, services, and solutions to integral enterprises and public sector organizations — helping them harness AI to drive outcomes at a time of exponential change with speed. With deep expertise in Managed Infrastructure Services, Application Modernization, and Industry-Specific Software Solutions, DXC modernizes, secures, and operates some of the world's most complex technology estates. Learn more on dxc.com.

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📌 Senior Platform Engineer (Ciudad de México)
🏢 DXC Technology
📍 Ciudad de México