20 publicaciones de empleo destacadas de backend developer en ciudad de méxico, dif (¡Están contratando!)

AI Engineer
DXC Technology —Ciudad de México, CDMX
- Oracle
- Administración de personal
- Salesforce
Fullstack Senior
NEXU —Ciudad de México, CDMX
- Tiempo completo
- Azure
- DevOps
- SQL
Postulación rápida
Full-Stack Developer
Alcor —Ciudad de México, CDMX
- Azure
- Inglés
- C++
Postulación rápida
Software Engineer II with Accounting Experience (Mexico)
Sezzle —Ciudad de México, CDMX
- $2,800 a $6,000 por mes
- Tiempo completo
- Home Office (Desde casa)
- Informática
- DevOps
- Java
Senior Python Developer - Computer Vision & Image Forensics Specialist
IES-Systems —Colonia del Valle, CDMX
- $30,000 a $55,000 por mes
- Tiempo completo
- Inglés
- SQL
- Experiencia en operaciones bancarias
- Seguro de gastos médicos
Postulación rápida
Software Engineer Intern
Arkham Technologies —Ciudad de México, CDMX
- Trabajo híbrido
- Informática
- SQL
- Licenciatura
Postulación rápida
Senior Software Developer
GBS Group —Ciudad de México, CDMX
- Tiempo completo
- Home Office (Desde casa)
- Administración de personal
- DevOps
- Inglés
Postulación rápida
JR Cloud Developer
Orion Innovation —Ciudad de México, CDMX
- Lunes a viernes
- Trabajo híbrido
- Azure
- DevOps
- Ingeniería
Postulación rápida
13d
0146 - Android Frontend Mobile Developer Sr
ALTEN MÉXICO —Ciudad de México, CDMX
- Tiempo completo
- Trabajo híbrido
- Ingeniería
- Licenciatura
Postulación rápida
AI Engineer (Full-Stack)
Hire Overseas —Ciudad de México, CDMX
- Tiempo completo
- Home Office (Desde casa)
- DevOps
- Experiencia en bases de datos
- Habilidades de comunicación
- Vacaciones adicionales o permisos con goce de sueldo
Postulación rápida
Senior Python Engineer
Delinea —Ciudad de México, CDMX
- Tiempo completo
- Trabajo híbrido
- Azure
- Administración de personal
- Informática
- Seguro de vida
MEX - Full-Stack Developer
Tidal —Ciudad de México, CDMX
- $3,000 a $6,000 por mes
- Tiempo completo
- Inglés
- Experiencia en bases de datos
- JavaScript
Postulación rápida
Mobile App Developer - Mexico Residents
Arcadion —Colonia Polanco, CDMX
- Tiempo completo
- Home Office (Desde casa)
- Administración de personal
- Informática
- Inglés
Postulación rápida
Senior Full Stack Engineer (Python/React)
Forager —Ciudad de México, CDMX
- Tiempo completo
- Home Office (Desde casa)
- Administración de personal
- DevOps
- Sin título de educación superior
- Vacaciones adicionales o permisos con goce de sueldo
Postulación rápida
Technical Lead / Senior Full Stack Developer con IA
Stefanini Latam —Ciudad de México, CDMX
- DevOps
- Java
- SQL
6d
Senior Full Stack Developers - CFR
DNV —Ciudad de México, CDMX
- Tiempo completo
- Administración de personal
- Inglés
- Experiencia en bases de datos
- Seguro de vida
5d
Software Engineer II (Mexico)
Sezzle —Ciudad de México, CDMX
- $2,800 a $6,000 por mes
- Tiempo completo
- Home Office (Desde casa)
- Informática
- DevOps
- Experiencia en bases de datos
Full Stack Engineer
Alcor —Ciudad de México, CDMX
- Oracle
- Administración de personal
- Java
Postulación rápida
Python Developer Senior | Generación Documental Bancaria
Creativo Integra —Colonia Lomas de Chapultepec, CDMX
- $40,000 a $50,000 por mes
- Tiempo completo
- Trabajo híbrido
- Azure
- Inglés
- SQL
- Seguro de gastos médicos
- Seguro de vida
Postulación rápida
0145 - IOS Frontend Mobile Developer (React Native) Sr
ALTEN MÉXICO —Ciudad de México, CDMX
- Tiempo completo
- Trabajo híbrido
- Ingeniería
- Licenciatura
- Experiencia en operaciones bancarias
Postulación rápida

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AI Engineer

DXC Technology -
Ciudad de México, CDMX

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Información del empleo

Cualificaciones

Modelos de inteligencia artificial
Oracle
Administración de personal
Node.js
Derecho
Kubernetes
Salesforce
.NET Core
C#
Microsoft SQL Server
.NET
SQL
Docker
JavaScript
PostgreSQL
TypeScript
Python

Descripción completa del empleo

Job Description:

About DXC

DXC Technology helps global organizations run their mission-critical systems while modernizing IT, optimizing data architectures, and accelerating innovation through cloud, automation, and Artificial Intelligence.

We are looking for an experienced AI Platform Engineer to lead the design and implementation of the AI foundation for a next-generation enterprise CPQ platform. This role combines AI engineering, data architecture, LLM integration, and platform modernization to transform a highly complex legacy application into an AI-native solution.

This is not simply an AI integration role—it is an opportunity to define how AI becomes a core part of the platform architecture, enabling intelligent decision-making, automation, and scalable enterprise solutions.

Required Technical Skills

Python – primary development language for AI/ML systems, LLM orchestration (LangChain, LlamaIndex), data pipelines, embedding generation, vector operations, and rapid prototyping; required across all three roles but primary here
Prompt engineering – crafting precise, constraint-rich prompts and AI constitutions (CLAUDE.md-style rule files) that direct AI behaviour reliably
LLM integration – Claude, GPT models, GitHub Copilot; building AI-augmented workflows and agentic systems for enterprise applications
MCP (Model Context Protocol) – building tool-use interfaces and AI skill development that give LLMs structured access to our current CPQ tool’s services and databases
Vector databases – embedding-based retrieval (Pinecone, Weaviate, pgvector) for knowledge management, documentation search, and institutional memory
JSON – schema design for AI tool definitions, MCP interfaces, LLM function calling specifications, structured output parsing, and agent configuration
Markdown – AI constitution authoring (CLAUDE.md files), prompt templates, knowledge base structuring, and documentation-as-code
Data modelling for AI – designing data structures and schemas that LLMs can reason over effectively; understanding complex existing relationships (costing WBS trees, commodities, elements, financial factors, bid history) to build reliable AI context
Data quality for AI – assessing, profiling, and improving data quality upstream of AI systems; understanding that poor data quality produces confidently wrong AI outputs
Analytical data design – structuring analytical datasets and knowledge bases from Oracle / MSSQL / ClickHouse sources for AI consumption
GitHub Copilot and Claude Code – not just using them, but designing how the broader team uses them; the AI toolchain is part of your architecture responsibility

Advantageous Skills

C# / .NET Core – understanding existing backend for integration and migration planning
JavaScript / TypeScript – for AI-powered frontend features or Node.js-based AI middleware
SQL (Oracle, MSSQL, PostgreSQL, ClickHouse) – for building AI context from existing databases and designing analytical schemas
Docker / Kubernetes – containerising AI services for deployment on EKS
Grafana / observability tooling – for AI performance monitoring and anomaly detection pipelines
RAG (Retrieval-Augmented Generation) – architecture patterns at scale; experience with enterprise RAG deployments
Fine-tuning, RLHF, evaluation frameworks – RAGAS, DeepEval for systematic AI output quality measurement
Event-driven architectures – designing AI agents that respond to system events from our existing message bus
Salesforce Einstein AI or similar enterprise AI platforms
dbt, Great Expectations or similar data quality tooling – for building systematic data quality checks upstream of AI models

AI-First Cognitive Requirements

Evaluative cognition shift – deep understanding that this role exists to help the team transition from generative to evaluative work modes; you design the systems that make evaluation possible
Sycophancy detection – understanding when AI agrees with framing because you're the prompter, not because you're right; designing systems that resist circular validation
Constitution design expertise – the highest-leverage artefact in AI-first development; a garbage constitution means a confidently wrong system
Adversarial verification design – creating structured exercises and automated checks that train evaluative instincts across the team
Data-chain awareness – the ability to trace an AI output back through its data sources, embeddings, and context to diagnose why it went wrong; never accepting “the AI said so” without understanding the data path

Key Responsibilities

Design and own the data foundations for AI – modelling existing costing structures, bid history, and financial factors into AI-consumable schemas; data quality is the prerequisite for every AI output
Build data profiling and quality assessment pipelines – understanding what data we currently have, what is reliable, and what must be cleaned or restructured before AI can use it
Design LLM-based replacements for rigid legacy business logic – costing rules, allocation algorithms, and financial calculations expressed as AI-driven decision systems
Build MCP-based AI skills that give LLMs structured access to current services, databases, and business logic – creating the foundation for an AI-native platform
Design and implement AI constitutions and guardrails encoding domain rules, pricing logic constraints, audit requirements, and data quality checks
Develop vector-based knowledge retrieval systems for documentation, architecture decisions, bid history, and institutional knowledge
Create AI-augmented developer tooling – specification templates, automated verification pipelines, and AI-assisted code review that catches “looks right vs. is right” failures
Design and build an AI-driven workflow engine to replace complex legacy orchestration patterns (125+ rigid service chains) with intelligent, self-adapting agents Establish metrics and measurement for AI-first adoption and platform modernisation progress
Support the team's transition to the Intent Generate Verify Decide Document workflow loop
Prototype and validate next-generation architecture patterns – proving that AI-native approaches can replace current complexity

Knowledge Transfer & Key-Person Risk Mitigation

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

Run regular AI literacy sessions with the whole team, including the two testers who are natural candidates for AI verification and prompt engineering backup roles
Train both testers on AI constitution design and adversarial verification techniques so that AI guardrail maintenance does not depend on a single person
Document all data models, embedding schemas, MCP tool definitions, and vector retrieval configurations in version-controlled Markdown; every AI skill must have a corresponding specification document
Pair with Position 1 (backend) to jointly own the data modelling decisions for the replacement platform; data architecture knowledge must overlap with at least one backend developer
Establish a “AI knowledge base” in the team's wiki covering prompt patterns, constitution templates, and data quality rules, accessible and maintainable by the whole team within 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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backend developer empleos en ciudad de méxico, dif

AI Engineer

Fullstack Senior

Full-Stack Developer

Software Engineer II with Accounting Experience (Mexico)

Senior Python Developer - Computer Vision & Image Forensics Specialist

Software Engineer Intern

Senior Software Developer

JR Cloud Developer

0146 - Android Frontend Mobile Developer Sr

AI Engineer (Full-Stack)

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MEX - Full-Stack Developer

Mobile App Developer - Mexico Residents

Senior Full Stack Engineer (Python/React)

Technical Lead / Senior Full Stack Developer con IA

Senior Full Stack Developers - CFR

Software Engineer II (Mexico)

Full Stack Engineer

Python Developer Senior | Generación Documental Bancaria

0145 - IOS Frontend Mobile Developer (React Native) Sr

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About DXC

Required Technical Skills

Python – primary development language for AI/ML systems, LLM orchestration (LangChain, LlamaIndex), data pipelines, embedding generation, vector operations, and rapid prototyping; required across all three roles but primary here

Advantageous Skills

C# / .NET Core – understanding existing backend for integration and migration planning

AI-First Cognitive Requirements

Evaluative cognition shift – deep understanding that this role exists to help the team transition from generative to evaluative work modes; you design the systems that make evaluation possible

Key Responsibilities

Design and own the data foundations for AI – modelling existing costing structures, bid history, and financial factors into AI-consumable schemas; data quality is the prerequisite for every AI output

Knowledge Transfer & Key-Person Risk Mitigation

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

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