Applied LLM Engineer

Simple Solutions • San Francisco, CA

2d • $180 - $200 • Onsite

About The Position

Qualifications Bachelor’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 applications Strong backend engineering experience, including: Building APIs from the ground up using Python frameworks such as FastAPIand Django Deploying 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 Pulumi Hands-on experience building LLM-based applications, including: Designing multi-step LLM workflowsand task-specific agents Experience working with most frontier models (e.g. OpenAI, Anthropic, Google, etc…) Experience with AI tools as a user, specifically AI code editors Developing advanced prompt engineering strategies, evaluation frameworks, and RAG pipelines Conducting technical R&D to explore and define the boundaries of model functionality Familiarity 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 plus Frontend development experience (e.g., React) is a plus, but not required Requirements Ability to design and maintain scalable, production-grade backend systems for AI applications Ability to create, orchestrate, and evaluate LLM-based agentsand chained workflowswith minimal oversight Ability 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 functionality Ability to stay current with emerging practices, models, and tooling in the generative AI ecosystemand apply them pragmatically Ability 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 Information Very comfortable working in a fast-paced and intense startup environment Willing to work in-person in our office in Mission Bay 4-5 days/week Likes 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?

Requirements

• 3+ years of software development experience, with a focus on building and deploying AI/ML applications
• Strong backend engineering experience, including: Building APIs from the ground up using Python frameworks such as FastAPIand Django
• Deploying 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 Pulumi
• Hands-on experience building LLM-based applications, including: Designing multi-step LLM workflowsand task-specific agents
• Experience working with most frontier models (e.g. OpenAI, Anthropic, Google, etc…)
• Experience with AI tools as a user, specifically AI code editors
• Developing advanced prompt engineering strategies, evaluation frameworks, and RAG pipelines
• Conducting technical R&D to explore and define the boundaries of model functionality
• Familiarity with secure coding practices, ideally in regulated industries (e.g., life sciences, healthcare, fintech)
• Ability to design and maintain scalable, production-grade backend systems for AI applications
• Ability to create, orchestrate, and evaluate LLM-based agentsand chained workflowswith minimal oversight
• Ability 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 functionality
• Ability to stay current with emerging practices, models, and tooling in the generative AI ecosystemand apply them pragmatically
• Ability 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

Nice To Haves

• Experience working in or adjacent to regulated domains(life sciences, clinical R&D) is a plus
• Frontend development experience (e.g., React) is a plus, but not required

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level