Senior Platform Engineer

DXC Technology Inc.; Ciudad de México; Presencial; MXN 1.200.000 - 2.000.000; Jornada completa

Descripción de la vacante

DXC Technology invites experienced backend engineers to design and implement modern Python/TypeScript services. You will decompose a 34-microservice landscape and build AI-assisted replacements while ensuring data quality and scalable architectures.

The role emphasizes AI-first development, LLM integration, event-driven patterns, and migration pathways. You will collaborate cross-functionally to deliver reliable, observable systems and document architectural decisions in ADRs.

Formación

• 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 with DB specialist, document ADRs, run architecture clinics, maintain runbooks, cross-train frontend developers.

Responsabilidades

• 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 with DB specialist, document ADRs, run architecture clinics, maintain runbooks, cross-train frontend developers.

Conocimientos

C# / .NET

Python

JavaScript / TypeScript

JSON

Markdown

Docker / Kubernetes

SQL Server

Oracle RDS

PostgreSQL

Data modelling

GitHub Copilot

Claude Code

LLM integration

API design

Herramientas

Jenkins

GitLab

Grafana

Kibana

ELK stack

Kafka

EventBridge

Redis

RabbitMQ

MassTransit

ClickHouse

Angular

React

Descripción del empleo

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 global 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.