Senior MLE Staff Engineer (GenAI Platform)
Clarity AI
| Company | Clarity AI |
| Category | Engineering |
| Location | Madrid, Spain |
| Remote | On-site (inferred) |
| Employment | Not stated |
| Level | Lead |
| Salary | Not stated by the employer |
| Posted | 9 Apr 2026 |
| Last verified | 12 Aug 2026 |
| Source | The employer's own careers page (company_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