Clarity AI - Senior Staff GenAI Platform Engineer
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Requirements
• LLM & Agent Expertise: Deep, hands-on experience deploying Large Language Models and complex agentic architectures at scale. • LLM & Agent Expertise: • GenAI Platform Specifics: Proven experience in implementing Prompt Lifecycle Management (versioning, testing, and deploying prompts as code), an LLM Abstraction Layer (provider-agnostic access), and systems for Cost & Usage Control (visibility and limits on GenAI spend per use case). • GenAI Platform Specifics: • Prompt Lifecycle Management • LLM Abstraction Layer • Cost & Usage Control • Evaluation & Benchmarking Mastery: Expert-level experience building automated evaluation pipelines and frameworks (e.g., Ragas, DeepEval, G-Eval) and implementing LLM-as-a-judge patterns to validate model quality, grounding, and safety in CI/CD. • Evaluation & Benchmarking Mastery: • LLM-as-a-judge • Platform & MLOps Mindset: A proven track record of building platforms or shared infrastructure. Deep understanding of MLOps concepts like Model Registry (versioning, state management, and lineage) and Model Monitoring & Drift Detection. • Platform & MLOps Mindset: • Model Registry • Model Monitoring & Drift Detection • 3+ years of experience in MLOps or high-scale Software Engineering with a focus on AI production environments. • Technical Stack Mastery: Expert-level Python and deep experience with container orchestration (Kubernetes, Docker) and cloud infrastructure (AWS/GCP). • Technical Stack Mastery: • Kubernetes • Docker • AI Tooling & Frameworks: Proficiency with orchestration libraries (e.g., LangChain, LlamaIndex, CrewAI), vector databases (e.g., Pinecone, Weaviate), and inference engines (e.g., vLLM, TGI). • AI Tooling & Frameworks: • Agility & Adaptability: The ability to learn and implement new technologies in a field that changes weekly. You should be a "fast mover" who enjoys constant evolution. • Agility & Adaptability: • Software Excellence & Governance: Strong fundamentals in API design, microservices, and "GitOps" methodologies, including the implementation of automated security and compliance by default. • Software Excellence & Governance: • English Proficiency: Excellent communication skills (minimum C1 level), with the ability to articulate technical vision to both engineers and leadership. • English Proficiency:
Responsibilities
• As a Senior GenAI Platform Staff Engineer, you will be responsible for: • GenAI Platform Engineering: Designing and developing the core platform that enables the efficient deployment, scaling, and management of LLMs and multi-agent systems. • GenAI Platform Engineering: • Infrastructure for Agents: Building specialized infrastructure to support long-running agentic workflows, including state management, tool-calling interfaces, and complex reasoning loops. • Infrastructure for Agents: • High-Scale Productionization & Model Serving: Scaling inference for LLMs to handle global demand while optimizing for latency, throughput, and cost. Implement standard batch and online serving with controlled rollback. • High-Scale Productionization & Model Serving: • Build & Delivery: Establishing the "Golden Path" for model deployment through a self-service path to move code, data, and models to production safely and reproducibly, including automated evaluation frameworks, safety guardrails, and CI/CD/CT pipelines. • Build & Delivery • self-service path to move code, data, and models to production safely and reproducibly • Strategic Vision & Product Management: Continuously monitoring the AI ecosystem and proactively evolving our platform to maintain a competitive edge. This includes adopting best practices in Platform Product Management and driving the adoption of golden-path solutions. • Strategic Vision & Product Management: • Platform Product Management • adoption of golden-path solutions • End-to-End Observability: Implementing deep observability for LLMs, tracking not just system health but providing unified visibility into health, impact, and root cause across data, ML, and GenAI (including model hallucinations, token usage, and RAG performance). • End-to-End Observability: • unified visibility into health, impact, and root cause across data, ML, and GenAI • Collaborative Foundation: Providing the tools and abstractions that allow Data Scientists and stakeholders to move from a "tuned model" to a "production service" with zero friction. • Collaborative Foundation: • Location • The role is based in Madrid ( Remote / Hybrid )
Benefits
• Competitive compensation, both in terms of base salary as well as equity plans that enable to you to share in our success • Flexibility in ways of working both in terms of your schedule as well as your location, whether you prefer to work from home, the office, or abroad with access to a global network of co-working spaces • Flexibility • Generous paid time off schemes, including vacation, sabbatical, religious observance and compensation days • Generous paid time off schemes • Meaningful benefits including private healthcare coverage, fitness and wellness programs covered through Wellhub, working-from-home allowances to help you set up your home office and cover monthly expenses • Professional development with annual training budget for conferences, courses, certifications and access to top market e-learning platforms • Professional development • Collaborative environment with multiple offices around the globe, regular team activities and events as well as employee-led resource groups • Collaborative environment
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