About Clarity AI
Clarity AI is a global tech company founded in 2017 with a unique mission: bringing societal impact to markets. We leverage AI and machine learning technologies to provide top international investors, governments, companies, and consumers with the right data, methodologies, and tools to make more informed decisions. We are now a team of more than 300 highly passionate and curious individuals from all over the world, with offices in New York, Madrid, London, Paris, and Abu Dhabi. Together, we have established Clarity AI as a leading sustainability tech AI company backed by investors and strategic partners such as BlackRock, SoftBank, and Deutsche Börse, who believe in us and share our goals. We are dedicated to cultivating an exceptional workplace environment, and we take pride in our culture, defined by our commitment to being fact-based, diverse, transparent, meritocratic, and flexible. We have plans to continue growing our teams globally, so if you would like to join us on this rocket ship, keep reading! Your work will shape and guide the sustainable decisions of investors, companies and consumers worldwide.
About The Role
We are looking for a Senior MLE Staff Engineer who is an expert in the deployment and scaling of LLMs and Agentic systems. In this role, you will bridge the gap between machine learning experimentation and production at scale by building the robust, highly efficient platform that powers our AI initiatives. While our Data Science teams focus on developing and tuning state-of-the-art models, you will be the owner of the platform that enables them. You will define best practices, build automated pipelines, and ensure that our infrastructure can handle complex agentic workflows with high reliability and performance. This is a role for a visionary who stays ahead of the daily shifts in the AI landscape and can rapidly adapt our stack to leverage emerging trends.
What You’ll Be Doing
- GenAI Platform Engineering: Designing and developing the core platform that enables the efficient deployment, scaling, and management of LLMs and multi-agent systems.
- Infrastructure for Agents: Building specialized infrastructure to support long-running agentic workflows, including state management, tool-calling interfaces, and complex reasoning loops.
- 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.
- 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.
- 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.
- 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).
- 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.
What You’ll Need
- LLM & Agent Expertise: Deep, hands-on experience deploying Large Language Models and complex agentic architectures at scale.
- 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).
- 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.
- 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.
- 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).
- 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).
- 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.
- Software Excellence & Governance: Strong fundamentals in API design, microservices, and "GitOps" methodologies, including the implementation of automated security and compliance by default.
- English Proficiency: Excellent communication skills (minimum C1 level), with the ability to articulate technical vision to both engineers and leadership.