{"id":62274,"url":"https://alion.io/job/dxc-technology-ai-engineer","title":"AI Engineer","company":{"id":88,"name":"DXC Technology","domain":"dxc.com","url":"https://alion.io/company/dxc-technology","size_band":"5000+","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":85,"open_postings":178,"ghost_share":0,"stale_share":0.798,"repost_share":0.011,"time_to_fill_p50_days":20,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Mexico City, Mexico"],"countries":["MX"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":".NET","optional":false},{"name":"AI Agents","optional":false},{"name":"Amazon EKS","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"C#","optional":false},{"name":"Claude","optional":false},{"name":"Claude Code","optional":false},{"name":"ClickHouse","optional":false},{"name":"Copilot","optional":false},{"name":"dbt","optional":false},{"name":"DeepEval","optional":false},{"name":"Docker","optional":false},{"name":"Embeddings","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Function Calling","optional":false},{"name":"Grafana","optional":false},{"name":"Great Expectations","optional":false},{"name":"JavaScript","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"LlamaIndex","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"MS SQL","optional":false},{"name":"Node JS","optional":false},{"name":"pgvector","optional":false},{"name":"Pinecone","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"RLHF","optional":false},{"name":"SQL","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Tool Use","optional":false},{"name":"TypeScript","optional":false},{"name":"Weaviate","optional":false},{"name":"AWS","optional":true}],"status":"live","first_seen_at":"2026-08-18T00:00:00Z","employer_posted_date":"2026-08-18","last_verified_at":"2026-10-17T05:37:59Z","board_verified":true,"closed_at":null,"days_open":60,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":60},"description":"Job Description:\nAbout DXC\nDXC Technology helps global organizations run their mission-critical systems while modernizing IT, optimizing data architectures, and accelerating innovation through cloud, automation, and Artificial Intelligence.\nWe 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.\nThis 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.\nRequired Technical Skills\nPython - 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\n\nPrompt engineering - crafting precise, constraint-rich prompts and AI constitutions (CLAUDE.md-style rule files) that direct AI behaviour reliably\n\nLLM integration - Claude, GPT models, GitHub Copilot; building AI-augmented workflows and agentic systems for enterprise applications\n\nMCP (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\n\nVector databases - embedding-based retrieval (Pinecone, Weaviate, pgvector) for knowledge management, documentation search, and institutional memory\n\nJSON - schema design for AI tool definitions, MCP interfaces, LLM function calling specifications, structured output parsing, and agent configuration\n\nMarkdown - AI constitution authoring (CLAUDE.md files), prompt templates, knowledge base structuring, and documentation-as-code\n\nData 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\n\nData quality for AI - assessing, profiling, and improving data quality upstream of AI systems; understanding that poor data quality produces confidently wrong AI outputs\n\nAnalytical data design - structuring analytical datasets and knowledge bases from Oracle / MSSQL / ClickHouse sources for AI consumption\n\nGitHub 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\n\nAdvantageous Skills\nC# / .NET Core - understanding existing backend for integration and migration planning\n\nJavaScript / TypeScript - for AI-powered frontend features or Node.js-based AI middleware\n\nSQL (Oracle, MSSQL, PostgreSQL, ClickHouse) - for building AI context from existing databases and designing analytical schemas\n\nDocker / Kubernetes - containerising AI services for deployment on EKS\n\nGrafana / observability tooling - for AI performance monitoring and anomaly detection pipelines\n\nRAG (Retrieval-Augmented Generation) - architecture patterns at scale; experience with enterprise RAG deployments\n\nFine-tuning, RLHF, evaluation frameworks - RAGAS, DeepEval for systematic AI output quality measurement\n\nEvent-driven architectures - designing AI agents that respond to system events from our existing message bus\n\nSalesforce Einstein AI or similar enterprise AI platforms\n\ndbt, Great Expectations or similar data quality tooling - for building systematic data quality checks upstream of AI models\n\nAI-First Cognitive Requirements\nEvaluative 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\n\nSycophancy detection - understanding when AI agrees with framing because you're the prompter, not because you're right; designing systems that resist circular validation\n\nConstitution design expertise - the highest-leverage artefact in AI-first development; a garbage constitution means a confidently wrong system\n\nAdversarial verification design - creating structured exercises and automated checks that train evaluative instincts across the team\n\nData-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\n\nKey Responsibilities\nDesign 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\n\nBuild 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\n\nDesign LLM-based replacements for rigid legacy business logic - costing rules, allocation algorithms, and financial calculations expressed as AI-driven decision systems\n\nBuild MCP-based AI skills that give LLMs structured access to current services, databases, and business logic - creating the foundation for an AI-native platform\n\nDesign and implement AI constitutions and guardrails encoding domain rules, pricing logic constraints, audit requirements, and data quality checks\n\nDevelop vector-based knowledge retrieval systems for documentation, architecture decisions, bid history, and institutional knowledge\n\nCreate AI-augmented developer tooling - specification templates, automated verification pipelines, and AI-assisted code review that catches “looks right vs. is right” failures\n\nDesign 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\n\nSupport the team's transition to the Intent → Generate → Verify → Decide → Document workflow loop\n\nPrototype and validate next-generation architecture patterns - proving that AI-native approaches can replace current complexity\n\nKnowledge Transfer & Key-Person Risk Mitigation\nThis role is responsible for reducing its own bus factor. Concrete expectations:\nRun regular AI literacy sessions with the whole team, including the two testers who are natural candidates for AI verification and prompt engineering backup roles\n\nTrain both testers on AI constitution design and adversarial verification techniques so that AI guardrail maintenance does not depend on a single person\n\nDocument 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\n\nPair 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\n\nEstablish 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\n\nAt 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.\nRecruitment 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 .","description_format":"text","description_chars":8222,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Managed IT Services (MSP)","Systems Integrators"],"lifecycle":[{"event":"open","at":"2026-08-18T00:00:00Z"}],"visa":[],"liveness":{"score":6,"band":"cold","label":"Long shot","p_open":1,"p_active":0.215,"p_room":0.28,"age_days":60,"expected_fill_days":20,"reasons":["conf:0","stale_co","velocity","win:tail","crowd:brand"],"computed_at":"2026-10-17T05:46:00Z"},"pay":null,"html_url":"https://alion.io/job/dxc-technology-ai-engineer","json_url":"https://alion.io/job/dxc-technology-ai-engineer.json","meta":{"generated_at":"2026-10-17T10:48:56Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":14168,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-18T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":88},"rest":"https://alion.io/mcp/rest/get_company?id=88"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fdxc-technology-ai-engineer"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Fdxc-technology-ai-engineer"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Fdxc-technology-ai-engineer"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/dxc-technology-ai-engineer\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fdxc-technology-ai-engineer"}]}