AI Engineer – Cloud

Job not on LinkedIn

🔥 1 minute ago

🇺🇸 United States – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

☁️ Cloud Engineer

👻 Ghost score 15%

What it means: the chance that no one is really hiring for this job.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Factorial

WebsiteLinkedInAll Job Openings

501 - 1000 employees

Founded 2016

👥 HR Tech

☁️ SaaS

🤝 B2B

💰 $120M Debt financing on 2025-04

HR Tech • SaaS • B2B

Factorial is a provider of HR-focused software delivered as a web/cloud product that supports recruitment, onboarding and broader people-operation workflows. The available content emphasizes recruitment and onboarding workflows, job postings and international/remote hiring, and includes privacy/cookie notices consistent with a customer-facing SaaS application. Factorial appears to serve businesses (B2B) with a platform for managing HR processes and supporting distributed teams.

📋 Description

• Design, build, and maintain AI systems that turn customer conversations and uploaded documents into grounded, auditable cost estimates • Build and maintain LangGraph agents, including ReAct retrieval sub-agents, custom middleware, Pydantic-typed graph state, and streaming flows • Own retrieval quality end to end through agentic retrieval, collection routing, LLM-driven metadata filters and similarity thresholds, query rewriting, semantic chunking, and citation tracking • Integrate AWS Bedrock, OpenAI, and self-hosted vLLM behind an abstract interface • Develop and maintain platform API services, relational and domain-knowledge data stores, vector stores, and AI workflow observability/tracing • Set technical direction for the AI workstream • Review designs and code, mentor engineers, and align with product stakeholders • Diagnose production AI behavior and deliver measurable improvements to accuracy, latency, cost, reliability, and grounding • Deliver roadmap commitments and make technical and roadmap trade-offs

🎯 Requirements

• Bachelor's degree in Computer Science, Software Engineering, Data Science, or Mathematics • Minimum of 6 years of progressive software engineering experience • 1+ years building production LLM-powered or machine-learning systems, including agents, RAG, or prompt/evaluation pipelines • Proven delivery in a fast-paced environment • Availability to overlap U.S./Eastern-Time business hours for client meetings • Expert production software engineering skills • Expertise building LLM-powered systems, agents/orchestration, retrieval-augmented generation, and prompt/evaluation pipelines • Fluency using AI development tools • Deep understanding of retrieval and grounding, embeddings and vector search, multi-model integration, and cost/latency tuning • Working knowledge of the cost-estimation / Work Breakdown Structure domain, or ability to learn a deep domain quickly • Ability to analyze complex, non-deterministic AI behavior from production traces and deliver scalable, well-tested solutions under time pressure • Exceptional ability to communicate technical and model-behavior concepts clearly to non-technical and client stakeholders • Confidence in live U.S./ET client meetings • Proven ability to make critical, time-sensitive decisions with limited information • High ownership, motivation, and bias to action • Good leadership skills • Desirable master's degree in Computer Science, Machine Learning, or AI • Desirable relevant cloud or machine-learning certification • Optional experience with self-hosted model inference, observability and tracing tooling, clean layered service architecture, internal AI tooling/automation, cloud platforms, agent-reliability patterns, document ingestion and processing, evaluation/benchmark frameworks, U.S.-based clients, mentoring, design reviews, technical direction, and AI roadmap trade-offs

🏖️ Benefits

• Flexible ways of working • Long-term development support • Collaboration, trust, quality, and strong systems-focused environment • Autonomy with accountability and influence over architecture, technical direction, and standards

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