Senior AI Engineer - LLM Agents & Platform (Remote)

Smartsheet; United States; Remote; USD 175,000 - 245,000; Full time

Benefits offered by this job

Teleworking options from US registered

Udemy online courses access

Parental leave

Job summary

Smartsheet seeks an experienced AI/ML engineer to design, build, and operate production-grade LLM-powered agents and workflows within SmartAssist, collaborating with Engineering and AI Platform teams to advance an Agent Development Lifecycle.

You will own prompts, RAG pipelines, model governance, and evaluation frameworks, mentor peers, and translate business needs into scalable AI solutions deployed on cloud infrastructure (AWS/GCP/Azure), with a strong focus on reliability and responsible AI.

Qualifications

• Minimum 8 years of software experience with 2+ years in production LLMs.
• Deep hands-on prompt engineering and context design skills.
• Strong knowledge of RAG, embeddings, and retrieval evaluation.
• Experience building or extending LLM evaluation frameworks.
• Fluency in agent system design and strong Python skills.

Responsibilities

• Design and build production-grade LLM-powered agents and workflows.
• Develop prompts, RAG pipelines, and agent reasoning patterns.
• Implement end-to-end AI systems on cloud infrastructure (AWS/GCP/Azure).
• Collaborate with engineering, product, and AI platform teams to deliver impact.
• Mentor engineers on ML best practices and production considerations.
• Establish best practices for AI development and model governance.

Skills

LLM experience

Prompt engineering

Python

RAG architectures

Agent system design

Model governance

Cross-functional collaboration

Evaluation frameworks

Databricks/Delta

Education

BS or MS in Computer Science

Tools

MLflow

CI pipelines

Multi-agent frameworks

Job description

Smartsheet seeks an experienced AI/ML engineer to design, build, and operate production-grade LLM-powered agents and workflows within SmartAssist, collaborating with Engineering and AI Platform teams to advance an Agent Development Lifecycle.

You will own prompts, RAG pipelines, model governance, and evaluation frameworks, mentor peers, and translate business needs into scalable AI solutions deployed on cloud infrastructure (AWS/GCP/Azure), with a strong focus on reliability and responsible AI.