San Francisco, CA | On-site | Full-time | $180,000-$400,000 per year | 2-8 years of experience | Visa sponsorship not available
About this role
This role is for a mid-level AI Engineer on Clera's core product team, building agentic systems that automate complex, multi-step workflows across regulated and enterprise domains. You'll work across the stack to ship production LLM-based services, ensure reliability and safety, and collaborate with founders, product, and design to deliver measurable user impact.
What you'll do
- Design, build, and maintain agentic systems that automate complex, multi-step workflows across healthcare, legal, fintech, logistics, and compliance.
- Own production retrieval-augmented generation (RAG) pipelines and retrieval infrastructure including vector DBs, 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 founders, product, and design to prioritize work, define success metrics, and iterate based on user feedback and telemetry.
What Clera is looking for
- 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 (e.g. LangGraph, CrewAI) and orchestration tools (e.g. Temporal, Trigger).
- 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.
Company
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