8 publicaciones de empleo destacadas de full stack en loreto, dif (¡Están contratando!)

Senior Platform Engineer
DXC Technology —Ciudad de México, CDMX
- Oracle
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Sr. Manager SRE (Individual Contributor)
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- Correo electrónico
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Sezzle —Ciudad de México, CDMX
- $5,000 a $9,500 por mes
- Tiempo completo
- Home Office (Desde casa)
- Informática
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- SQL
Desarrollador Full Stack Ssr - Publicación vencida
Wee Company —Benito Juárez, CDMX
- $25,000 a $28,000 por mes
DESARROLLADOR JAVA FULLSTACK SENIOR
Alia Integrando Talento —Ciudad de México, CDMX
- Tiempo completo
- Oracle
- Inglés
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- Oportunidades de crecimiento
- Seguro de vida
BI Platform
Alia Integrando Talento —Ciudad de México, CDMX
- Tiempo completo
- Trabajo híbrido
- Azure
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Full Stack Developer - Mexico Residents
Arcadion —Colonia Polanco, CDMX
- Tiempo completo
- Home Office (Desde casa)
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- Inglés
- Licenciatura
Postulación rápida
Desarrollador Full Stack
IDS Comercial, S.A. de C.V. —Benito Juárez, CDMX
- $30,000 a $34,000 por mes
- Tiempo completo
- Trabajo híbrido
- Java
- SQL
- JavaScript
Postulación rápida
1h

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Senior Platform Engineer

DXC Technology -
Ciudad de México, CDMX

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Información del empleo

Cualificaciones

Modelos de inteligencia artificial
Oracle
Node.js
Derecho
React
Kubernetes
.NET Core
C#
Microsoft SQL Server
.NET
Diseño de base de datos
Pandas
Docker
JavaScript
PostgreSQL
Redis
RabbitMQ
Redux
Angular
Kafka
GraphQL
RDS
Jenkins
TypeScript
Python

Descripción completa 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

This role is responsible for reducing its own bus factor. Concrete expectations:

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.

Recruitment fraud is a scheme in which fictitious job opportunities are offered to job seekers typically through online services, such as false websites, or through unsolicited emails claiming to be from the company. These emails may request recipients to provide personal information or to make payments as part of their illegitimate recruiting process. DXC does not make offers of employment via social media networks and DXC never asks for any money or payments from applicants at any point in the recruitment process, nor ask a job seeker to purchase IT or other equipment on our behalf. More information on employment scams is available here.

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full stack empleos en loreto, dif

Senior Platform Engineer

Sr. Manager SRE (Individual Contributor)

Senior Payments Engineer — Acquiring & Payment Processing

Desarrollador Full Stack Ssr - Publicación vencida

DESARROLLADOR JAVA FULLSTACK SENIOR

BI Platform

Full Stack Developer - Mexico Residents

Desarrollador Full Stack

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Required Technical Skills

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

Advantageous Skills

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

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

Key Responsibilities

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

Knowledge Transfer & Key-Person Risk Mitigation

This role is responsible for reducing its own bus factor. Concrete expectations:

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