Sr Applied LLM Engineer

Job Description Sr. Applied LLM Engineer QualificationsBachelor's or Master's degree in Computer Science, Engineering, or a related field (or equivalent practical experience)3+ years of software development experience, with a focus on building and deploying AI/ML applicationsStrong backend engineering experience, including:Building APIs from the ground up using Python frameworks such as FastAPI and DjangoDeploying and scaling containerized applications in cloud environments (e.g., AWS, GCP, Azure)Implementing CI/CD pipelines (e.g., GitHub Actions)Working with Infrastructure-as-Code tools such as Terraform or PulumiHands-on experience building LLM-based applications, including:Designing multi-step LLM workflows and task-specific agentsExperience working with most frontier models (e.g. OpenAI, Anthropic, Google, etc...)Experience with AI tools as a user, specifically AI code editorsDeveloping advanced prompt engineering strategies, evaluation frameworks, and RAG pipelinesConducting technical R&D to explore and define the boundaries of model functionalityFamiliarity with secure coding practices, ideally in regulated industries (e.g., life sciences, healthcare, fintech)Experience working in or adjacent to regulated domains (life sciences, clinical R&D) is a plusFrontend development experience (e.g., React) is a plus, but not required RequirementsAbility to design and maintain scalable, production-grade backend systems for AI applicationsAbility to create, orchestrate, and evaluate LLM-based agents and chained workflows with minimal oversightAbility to debug and improve LLM-driven systems, identifying issues across multiple layers (model output, API behavior, system logic)Ability to conduct rapid experimentation and research on LLM capabilities and translate findings into production functionalityAbility to stay current with emerging practices, models, and tooling in the generative AI ecosystem and apply them pragmaticallyAbility to communicate clearly with technical and non-technical collaborators (e.g., product managers, medical writers, customer teams)Ability to operate effectively in a fast-paced, ambiguity-heavy environment, managing shifting priorities and novel problem spaces Other InformationVery comfortable working in a fast-paced and intense startup environmentWilling to work in-person in our office in Mission Bay 4-5 days/weekLikes matcha KitKats, believes every LLM prompt is just Schrodinger's cat waiting to be observed, and knows too many random facts about the Mongol postal system Requirements Why are you interested in Artos? Have you successfully brought an LLM-based application to production for external customers? Are you comfortable with a high-intensity "startup hours" environment (roughly 12-14 hours/day) to hit aggressive growth targets? Preferred previous employers: Life Science Tech: Benchling, Viva. Legal/Compliance AI: Ironclad, Harvey. Open Source Communities: LangChain, LangGraph, or Maestro contributors.