{
  "id": "2110042793670717934",
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  "title": "Lead AI Engineer (Data & Automation)",
  "link": "https://www.upwork.com/jobs/~022110042793670717934",
  "description": "Lead AI Engineer (Data & Automation)\n\nAbout Skyward and the role\nSkyward is a SEO and AI-search agency that runs its client work on software it builds itself.\n\nAs Lead AI Engineer you are our primary engineer. You build and run the systems behind our delivery: a pipeline that audits a client's site and plans its content, the data platform under it, AI systems that search and write from each client's own knowledge, and the internal tools the team uses every day. You decide how things are built, keep costs in check, and explain your work to people who are not engineers.\n\nYou report to our Lead Strategist and Agency Owner.\n\nWhat you'll own\n\nThe AEO/SEO pipeline. Our core delivery engine. Build and maintain each phase: from audit and analysis through content enrichment, and the content-writing phase next.\n\nKeep a person in control: the pipeline stops for an operator's go or no-go before it spends money or moves to the next phase.\n\nMake every run traceable: what ran, with which settings, what it cost, and which version a reviewer signed off.\n\nOwn what clients receive from it: workbooks, reports and slide decks that are correct and look like Skyward made them.\n\nThe data platform.\nRun our data warehouse and database that holds clients, sites, pipeline state and deliverables.\n\nDesign tables, write migrations, and move data between systems without losing any.\nKeep production, development and experimental data apart, so a test never touches a client's results.\n\nAI systems.\nRun the client knowledge base: a searchable library of each client's documents, with a map of the entities in them and a check for contradictions.\n\nBuild AI content generation that stays grounded in sources and is fact-checked before it ships.\nPick the right model for each job, track what each call costs, and measure answer quality.\nShared platform and operations.\n\nMaintain the shared library every project is built on: credentials, data access, paid data sources with spend guards, and calls to several AI providers with cost tracking.\n\nBuild the engineering side of our internal operator dashboard.\n\nAdminister access, secrets and deployments across multiple platforms and keep cloud, data and AI spend in check.\n\nWorking with the team.\n\nBuild consistent data access and tools on top of the pipeline, so teammates' AI agents and workflows plug into the same data instead of each building their own.\n\nHelp non-developers contribute: they build skills and dashboard pages with AI tools, and you give them the patterns, review their pull requests and get their work safely into the main code.\n\nTurn prototypes the team builds with AI tools into systems that are tested and safe to rely on.\n\nHelp teammates get unstuck on new tools, and join client calls when the conversation turns to how our systems work.\n\nWhat you'll inherit\nYou take over working systems, not a blank page. Most of the code was written by directing an AI coding agent, with tests and design notes alongside.\n\nYour first 90 days\nDays 1–30: take ownership. Get access to every system, run the pipeline end to end on a live client, and read the design notes and runbooks. Ship one fix of your own.\n\nDays 31–60: validate the keyword and enrichment phase. Check its output against source data on live clients, from keyword clusters through the sitemap and content enrichment, and fix what you find.\n\nDays 61–90: start the content-writing phase. Design and begin building AI drafting that stays grounded in each client's knowledge base and is fact-checked before it ships.\n\nMust-haves\nStrong Python, and a data pipeline in production that other people depended on.\n\nSQL and data modelling in a cloud warehouse and in Postgres, including migrations on live data.\n\nDeploying and scheduling services on a cloud platform, with access and secrets handled properly. Ours is Google Cloud.\n\nProduction use of model APIs (OpenAI, Anthropic, Gemini or similar): structured inputs and outputs, cost tracking and quality checks.\n\nA retrieval-augmented generation (RAG) system you built and shipped, ideally combining semantic and keyword search with re-ranking.\n\nBuilding software day to day by directing an AI coding agent, and knowing how to check its work.\n\nNice to have\nEnough TypeScript and React to maintain an internal web app.\nSEO knowledge: crawls, keyword research, technical audits, content briefs.\nKnowledge graphs: entity extraction and resolving duplicates.\nClustering and embeddings for grouping keywords and pages.\n\nHow you work\nYou make architecture decisions on your own and explain the trade-offs in plain language.\n\nYou treat cost as a design constraint: paid calls are estimated before they run and logged after.\nYou write tests, design notes and change logs without anyone asking.\n\nHiring process\nIntro call (30 minutes) about you and what you have shipped.\n\nPaid take-home (about 4 hours): review a pull request written by an AI agent and say what you would change, then sketch a pipeline step that estimates and caps its own cost before calling a paid API.\n\nTechnical conversation (60 minutes): walk us through your take-home and one system you built.\n\nDecision within a week of the last conversation.\n\nTo apply, send a short note and links to systems you have built or shipped.",
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  "experience_level": "intermediate",
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  "duration": {
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    "weeks": 52
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  "workload": "More than 30 hrs/week",
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  "category": {
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  "created_at": "2026-10-13T16:19:39Z",
  "published_at": "2026-10-13T16:19:51Z",
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    "location": {
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      "country": "United States",
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