Founding Technical Lead, Applied AI

Witlore; Chicago (IL); On-site; USD 180,000 - 240,000; Full time

Job summary

Witlore is seeking a founding technical lead in Chicago to design the system, own the architecture, and drive the service to run autonomously across businesses. You will write the code, make the hard calls, deploy and monitor production systems, and raise the engineering standard while mentoring the team.

This on-site role demands deep technical judgment and accountable ownership of outcomes. This position blends hands-on development with strategic direction in a fast-growing AI-enabled services

Job description

Witlore is building professional services companies that run themselves. We automate the work to expand capacity, speed up delivery, and accelerate economic growth.

The founding technical lead will design the systems, own the architecture and make the service run

Own technical delivery inside the businesses we invest in. Write the code. Make the hard calls. Raise the team’s standard. Ship software people can rely on.

What you own

• Architect the whole system. Connect data, documents, AI models, APIs and existing software into systems that scale across businesses. Decide what runs autonomously, where people step in, and how results get checked.
• Ship it. Keep it running. Build critical components. Own deployment, monitoring, access controls and recovery. Get on site. Understand the work. When something fails, take responsibility.
• Raise the engineering standard. Review designs and code. Make engineers better. Turn proven solutions into reusable components. Make clear technical decisions and carry them into production.

Results that count

Reliable systems in daily use. Faster delivery. Lower cost per job. Reusable technology that carries proven improvements across businesses.

What you bring

• Proof in production. You have designed, shipped and operated systems customers rely on. Show the architecture. Explain the failure modes. Put numbers behind the business results.
• Technical judgment. Know when to use AI, conventional code or human review. Reason through data quality, evaluation, security, cost and performance. Defend the tradeoffs.
• Lead from the work. Write and debug code. Make clear decisions. Give direct feedback. Raise the capability of the engineers beside you.

Compensation

Initial equity: 0.5-2.0%.

Related roles and keywords

We welcome experience across these related roles, tools and workflows. Familiarity with every item is not required.

Automation and implementation roles: AI Automation Engineer; AI Automation Developer; AI Automation Specialist; AI Workflow Engineer; Workflow Automation Engineer; Automation Engineer; Business Process Automation Engineer; Intelligent Automation Engineer; RPA Developer; Integration Engineer; AI Integration Engineer; AI Implementation Engineer; Implementation Engineer; Solutions Engineer; AI Consultant; AI Automation Consultant; Low Code Developer; No Code Developer.

Career paths and builder backgrounds: Junior AI Engineer; Entry Level AI Engineer; Graduate AI Engineer; Junior Software Engineer; New Grad Software Engineer; Early Career Engineer; Associate Forward Deployed Engineer; self-taught developer; bootcamp graduate; freelance developer; independent builder; startup engineer; founding engineer; technical founder; open-source contributor; hackathon projects; AI-native; entrepreneurial; resourceful; self-directed; ownership; rapid learning; practical problem solving.

AI assisted development: AI coding tools; AI coding agents; AI-assisted software development; agentic coding; AI pair programming; vibe coding; rapid prototyping; debugging AI-generated code; code review; Cursor; Claude Code; OpenAI Codex; GitHub Copilot; Windsurf; Gemini CLI; Replit; Lovable; Bolt.new; v0.

AI models and application capabilities: artificial intelligence; machine learning; generative AI; large language models; LLMs; OpenAI API; Anthropic API; Claude; Google Gemini; Llama; Ollama; Hugging Face; model selection; prompt engineering; context engineering; function calling; structured outputs; JSON Schema; multimodal AI; natural language processing; NLP; information extraction; speech to text; text summarization; conversational AI; AI assistants; chatbots; voice agents.

Enterprise AI platforms and services: ChatGPT; Azure OpenAI; Azure Foundry; Microsoft Copilot; Palantir Foundry; Palantir AIP.

Agents retrieval and orchestration: AI agents; agentic AI; agentic workflows; agent orchestration; multi-agent systems; tool calling; agent memory; human-in-the-loop; Model Context Protocol; MCP; MCP servers; LangChain; LangGraph; LlamaIndex; Pydantic AI; CrewAI; retrieval augmented generation; RAG; embeddings; semantic search; vector databases; hybrid search; reranking; document chunking; knowledge bases; pgvector; Temporal.

Data spreadsheets and documents: data extraction; data ingestion; data cleaning; data validation; data quality; deduplication; data modeling; PostgreSQL; MySQL; SQLite; ETL; ELT; data pipelines; pandas; structured data; unstructured data; intelligent document processing; OCR; PDF parsing; document classification; Excel automation; Google Sheets automation; Google Apps Script; VBA; Power Query; email parsing; invoice extraction; document generation.

Business systems and integrations: CRM; ERP; accounting software; legacy business software; Microsoft 365; Google Workspace; Outlook; Gmail; Microsoft Teams; Slack; SharePoint; Google Drive; Salesforce; HubSpot; Microsoft Dynamics 365; QuickBooks; QBO; Xero; NetSuite; Sage; Zendesk; ServiceNow; ticketing systems; field service management; Power BI; reporting dashboards; HRIS; transportation management systems; TMS.

Deployment infrastructure and maintenance: production deployment; production AI; cloud deployment; self-hosting; Linux; Docker; AWS; Microsoft Azure; Google Cloud; GCP; serverless functions; CI/CD; GitHub Actions; system maintenance; incident response; technical documentation; runbooks; operational handover; production hardening; DevOps.

Testing reliability and security: automated testing; unit testing; integration testing; end-to-end testing; LLM evaluation; AI evals; evaluation datasets; output validation; hallucination detection; source citations; audit trails; human review; exception handling; retries; idempotency; logging; monitoring; observability; debugging; authentication; authorization; secrets management; data privacy; prompt injection defenses; cost optimization; latency optimization; AI guardrails; caching; acceptance criteria; edge-case testing.

Identity access and AI governance: AI governance; identity and access management; IAM; single sign-on; SSO; least privilege; RBAC.

Onsite delivery and process improvement: forward deployment; embedded engineering; onsite; on-site; on premises; frequent travel; customer-facing engineering; stakeholder interviews; employee observation; workflow mapping; process discovery; process mining; requirements gathering; solution design; opportunity prioritization; proof of concept; MVP; pilot deployment; user acceptance testing; UAT; change management; employee training; adoption; nontechnical communication; continuous improvement; root cause analysis; comfort with ambiguity; end-to-end delivery; systems thinking; knowledge transfer; customer empathy; SOP automation; go-live support.

Business workflows and measurable outcomes: sales automation; lead qualification; CRM data entry; quote generation; proposal automation; customer onboarding; customer support automation; invoice processing; accounts payable; accounts receivable; collections automation; reconciliation; bookkeeping automation; accounting automation; month-end close; financial reporting; document review; operations automation; back-office automation; procurement automation; order processing; scheduling automation; dispatch automation; inventory workflows; revenue growth; cost reduction; labor productivity; margin improvement; EBITDA improvement; cycle time reduction; error reduction; automation ROI; private equity; AI-native private equity; portfolio company operations; operational improvement; operational value creation; time savings; repeatable deployment patterns.

Accounting and finance workflows: general ledger automation; internal controls; financial close automation; job costing; work order costing; client billing; revenue cycle automation; finance transformation.