Lead AI Engineer

The Lead AI Engineer is a senior, hands‑on role for an engineer who pairs deep technical mastery with genuine curiosity about the business problems worth solving. Sitting at the center of Sigga’s AI strategy, this person designs and ships AI‑powered capabilities into our enterprise asset management products — and sets the technical bar for how the broader team builds. It is a builder‑leader position spanning the full lifecycle, from framing the right problems with stakeholders to architecting from first principles, writing production code, and shipping. AI‑assisted development (Claude Code, Codex, Cursor) is a first‑class tool here — a force multiplier you are expected to master, not an afterthought. Key Responsibilities Design and build AI‑powered capabilities into Sigga’s EAM platform — from classification and prediction models to retrieval‑augmented generation and agentic workflows Take features from concept to production: architecture, data pipelines, model integration, application code, evaluation, and deployment Integrate with SAP and other enterprise systems with clean API contracts and data flows that hold up at scale, plus evaluation and monitoring that keep AI features trustworthy Technical Leadership & Craft Set the technical bar — code quality, architecture, engineering practice — and raise it through review, pairing, and example Mentor engineers on first‑principles problem‑solving, not just framework familiarity; make pragmatic build‑vs‑buy and architecture calls Own ambiguous, high‑stakes technical problems end to end, without waiting to be directed AI-Accelerated Development Use Claude Code and AI‑assisted development as a genuine force multiplier — build the prompts, patterns, and workflows that let the whole team do the same, and stay on the cutting edge of LLM capabilities, agentic frameworks, and developer toolchains Business Partnership & Discovery Work directly with product, customers, and internal stakeholders to understand real operational problems before writing a line of code Run structured discovery — separate signal from noise, challenge assumptions, turn ambiguous asks into tightly scoped, measurable specs — then translate technical possibility into business value and compress feedback loops by shipping incrementally What Sets the Right Candidate Apart Strong technical foundation. Agentic coding tools let almost anyone touch a long list of technologies. The rare engineers understand systems from first principles — they can reason beneath the abstraction and debug when it breaks. Mastery of AI‑assisted development. The strongest candidates turn that foundation into extraordinary leverage with AI coding tools (Claude Code, Codex, Cursor), shipping more — and better — than teams several times their size. Business curiosity and literacy. The heads‑down coder is a thing of the past. The best engineers engage stakeholders directly, extract the real requirement behind the ask, and compress the loop between idea and impact. Qualifications Required 6+ years building and shipping production software, with real depth in at least one general‑purpose language (Python strongly preferred) Deep, first‑principles understanding of software and systems — able to reason from fundamentals rather than relying on framework familiarity Hands‑on experience building AI / ML or LLM‑powered features in production: RAG, tool use / agentic patterns, prompt engineering, and evaluation Fluency with AI‑assisted development tools (Claude Code, Codex, Cursor, or similar) as part of a daily workflow Strong cloud and integration experience (AWS, Azure, or GCP); comfort with REST APIs, relational databases, and data pipelines Demonstrated end‑to‑end ownership with minimal supervision, and excellent communication — equally credible with engineers and with executives or customers Preferred Experience setting technical direction or mentoring as a tech lead or senior IC Familiarity with enterprise software domains — SAP, ERP, or asset‑intensive operations (manufacturing, energy, utilities, mining) a strong plus Background in data science, ML engineering, or another quantitative discipline; experience in a high‑growth, product‑led environment #J-18808-Ljbffr