AI Engineer – Cloud

Job not on LinkedIn

🔥 14 hours ago

💃 Latin America – Remote

⏰ Full Time

🟡 Mid-level

🟠 Senior

☁️ Cloud Engineer

👻 Ghost score 12%

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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QED

WebsiteLinkedInAll Job Openings

11 - 50 employees

Founded 2014

🤖 Artificial Intelligence

☁️ SaaS

🤝 B2B

Artificial Intelligence • SaaS • B2B

QED is a global engineering partner that builds applied intelligence and deep-tech product engineering inside other companies' platforms, delivering air-gapped, audit-ready, and brand-embedded solutions across web, mobile and platform environments. They offer two engagement models — Discovery for established organisations needing outcome-first insights and governance, and Foundry for teams building new intelligent products and MVPs — and provide capabilities in AI engineering, data engineering, AI assurance, platform & managed services, and operational excellence. QED operates in high-stakes industries (safeguarding, education, employment, automotive, manufacturing, finance, legal, energy, smart buildings, IoT, decarbonisation, travel, logistics) and emphasizes real-time decision infrastructure, energy-aware AI, staged AI triage pipelines, data residency, and senior-led distributed engineering across offices in London, Zagreb, Belgrade and (soon) Zurich.

📋 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 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 • Maintain platform API services, relational and domain-knowledge data stores, vector store, and AI workflow observability/tracing • Set technical direction for the AI workstream • Review designs and code • Mentor engineers • Align with product stakeholders and make roadmap trade-offs • Analyse production traces and improve agent reliability, latency, LLM cost-efficiency, retrieval grounding, and estimate/sizing accuracy

🎯 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 • Deep understanding of retrieval and grounding, embeddings and vector search, multi-model integration, and cost/latency tuning • Working knowledge of cost-estimation / Work Breakdown Structure domain, or ability to learn a deep domain quickly • Ability to analyse complex, non-deterministic AI behaviour from production traces and deliver scalable, well-tested solutions under time pressure • Exceptional ability to communicate technical and model-behaviour concepts clearly to non-technical and client stakeholders • Confidence in live U.S./ET client meetings and disciplined async written communication across time zones • Proven ability to make critical, time-sensitive decisions with limited information • High ownership, motivation, bias to action, and willingness to go the extra mile • Good leadership skills • Optional: Master's degree in Computer Science, Machine Learning, or AI • Optional: Relevant cloud or machine-learning certification • Optional: Self-hosted model inference, observability and tracing tooling, clean layered service architecture, internal AI tooling/automation, cloud-platform depth, agent-reliability patterns, document ingestion across multiple formats, evaluation/benchmark frameworks, regression fixtures, direct U.S.-based client experience, mentoring, design reviews, technical direction, roadmap trade-offs, and high-growth environment experience

🏖️ Benefits

• Flexible ways of working • Long-term development • Collaboration, trust, and quality-focused work environment • Autonomy with accountability • Opportunity to influence architecture, technical direction, and standards • Mentoring and growth alongside experienced engineers

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