AI Engineer

Job Description:

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.

• 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

• 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

• 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

• 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

• 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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