Clera - AI Engineer (Mid-Level)
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Requirements
• 2–8 years of software engineering experience with demonstrated delivery of shipped user-facing or backend products. • Practical experience deploying LLMs or LLM-based services in production, including prompt design, orchestration, and tool integration. • Proficiency across the stack: Python plus TypeScript/React (or equivalent), and experience with cloud platforms (AWS or GCP) and relational or NoSQL databases. • Working knowledge of RAG patterns, vector databases, embeddings, and retrieval pipelines, with sound judgment to choose appropriate approaches. • Experience building automated tests, evaluations, and monitoring for AI systems to ensure reliability beyond demos. • Experience with agent or workflow frameworks and orchestration tools. • Familiarity with fine-tuning, parameter-efficient tuning, or multi-modal model integration. • Background building multi-tenant or enterprise-ready systems, or experience in regulated industries such as healthcare, fintech, or legal. • Experience designing API-driven, high-throughput systems and real-time product features. • Proven ownership delivering end-to-end features from data model to deploy and monitoring, with a user-centric and pragmatic engineering mindset. • Location • Remote or San Francisco, CA, United States.
Responsibilities
• Design, build, and maintain agentic systems that automate complex, multi-step workflows across healthcare, legal, fintech, logistics, and compliance domains. • Own production retrieval-augmented generation (RAG) pipelines and retrieval infrastructure including vector databases, embeddings, and indexing for domain-specific search at scale. • Implement multi-agent orchestration, tool-calling, memory, and reasoning components to deliver robust AI-driven user experiences. • Develop evaluation and safety infrastructure to measure model performance, surface regressions, and enforce enterprise-level trust and reliability. • Ship full-stack AI products from MVP to enterprise-grade by designing APIs and data models, implementing frontend and backend code, and operating production systems with CI/CD, monitoring, and testing. • Collaborate with leadership, product, and design to prioritize work, define success metrics, and iterate based on user feedback and telemetry.
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