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Senior MLE Staff Engineer (GenAI Platform)

Clarity AI
CompanyClarity AI
CategoryEngineering
LocationMadrid, Spain
RemoteOn-site (inferred)
EmploymentNot stated
LevelLead
SalaryNot stated by the employer
Posted9 Apr 2026
Last verified12 Aug 2026
SourceThe employer's own careers page (company_site)
Applications are handled by the employer, not by us.Apply on the employer's site →
Description
Senior MLE Staff Engineer (GenAI Platform) Clarity AI Madrid, Spain (Remote/Hybrid, CET +/- 2 hours) About the Company Clarity AI, founded in 2017, is a sustainability-focused tech firm with 300+ employees across five global offices. The organization leverages AI to help investors, governments, and companies make informed decisions. Major backers include BlackRock, SoftBank, and Deutsche Borse. The company emphasizes a fact-based, diverse, transparent, meritocratic, and flexible workplace culture. Key Responsibilities The role bridges ML experimentation and production by • GenAI Platform Engineering: Design systems for deploying LLMs and multi-agent solutions • Agent Infrastructure: Build systems supporting long-running workflows with state management, tool-calling interfaces, and complex reasoning loops • Model Serving: Scale inference globally while optimizing latency, throughput, and costs • Deployment Pipeline: Establish self-service pathways with automated evaluation, safety guardrails, CI/CD/CT pipelines, observability for hallucinations and RAG performance, and model registry management • Strategic Evolution: Monitor AI trends and upgrade platform capabilities continuously • Observability: Implement unified monitoring across data, ML, and GenAI layers • Enablement: Provide tools helping data scientists move from models to production services • Design prompt lifecycle management, LLM abstraction layers, and cost controls Required Qualifications • 3+ years MLOps or AI production engineering experience • Deep hands-on experience deploying LLMs and complex agentic architectures at scale • Expert-level evaluation frameworks (Ragas, DeepEval, G-Eval) with LLM-as-a-judge patterns • Expertise in prompt lifecycle management, LLM abstraction layers, cost controls • Model registry and drift detection understanding • Expert Python; Kubernetes/Docker mastery • AWS/GCP cloud infrastructure experience • Orchestration tools (LangChain, LlamaIndex, CrewAI); vector databases; inference engines (vLLM, TGI) • API design, microservices, GitOps fundamentals • C1-level English fluency Compensation & Benefits • Competitive base salary plus equity participation • Flexible scheduling and location options • Generous PTO including sabbatical options • Healthcare, wellness programs, home office allowances • Annual professional development budget • Global collaborative environment