Senior MLE Staff Engineer (GenAI Platform)
ExpiredThis listing is older than 60 days and may no longer be accepting applications.
This listing is older than 60 days and has likely been filled. Here are open roles like it:
YLD
Scale AI
Scale AI
Get new agentic engineering jobs in your inbox every Monday.
One curated email a week. No spam, unsubscribe anytime.
Agentic Frameworks
About the Role
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
