AI Engineer

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