AI Engineer

Mex - Dif - Mexico Citymid

Machine Learning EngineerData

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Overview

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,

Technical Tools

Machine Learning EngineerData

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.

Responsibilities

~1 min read

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dbt, Great Expectations or similar data quality tooling – for building systematic data quality checks upstream of AI models

Requirements

~1 min read

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

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

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.

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