Smartsheet is building the next generation of AI-powered strategic planning and work execution agents through SmartAssist, our intelligent agent platform. You will work closely with Engineering and AI Platform teams, embedded in a team working in a critical area and building toward a mature Agent Development Lifecycle (ADLC).

You will architect how we design, build, and operationalize AI-driven solutions that transform work execution at enterprise scale.

You Will:

• Design and build production-grade LLM-powered agents and workflows within Smartsheet, including architecture decisions on system design, data pipelines, and deployment strategies
• Develop and optimize prompts, RAG pipelines, and agent reasoning patterns; build evaluation frameworks to measure accuracy, hallucination rates, and performance across model versions
• Implement and manage end-to-end AI systems on cloud infrastructure (AWS, GCP, or Azure), including monitoring, optimization, and incident response
• Collaborate with Engineering, Product, and cross-functional teams to translate business requirements into technical AI solutions that drive real impact
• Mentor and guide other engineers on applied ML best practices, modern AI development patterns, and production considerations
• Establish best practices and standards for AI development, model governance, and responsible AI within the organization
• Stay current with the rapidly evolving AI/LLM landscape and evaluate emerging tools, frameworks, and techniques for Smartsheet's needs
• Contribute to a full stack application– both legacy and greenfield

You Have:

• 8+ years of software engineering experience, with at least 2 years working directly with LLMs in production
• Deep, hands-on experience with prompt engineering and context engineering, you understand how model behavior changes with framing, structure, and input design
• Strong working knowledge of RAG architectures: chunking strategies, embedding models, retrieval evaluation, and failure diagnosis
• Experience building or extending LLM evaluation frameworks, you have designed scorers, worked with golden datasets, and thought carefully about what good looks like
• Fluency in agent system design, you don't need to own the architecture, but you can engage as a peer on architectural tradeoffs that affect quality
• Strong Python skills; comfortable working in data-heavy environments (Databricks, Delta tables, or equivalent)
• Ability to communicate complex quality findings (written and verbal) to both technical and non-technical stakeholders, you can explain what’s broke, why it matters, and what needs to happen next without losing the room
• Strong cross-functional judgment, you know when to escalate, when to resolve independently, and how to build credibility across engineering, product, and AI platform teams
• A bias for clarity in ambiguous situations, when failure modes are murky and trade-offs are real, you bring structure and a clear point of view rather than waiting for consensus
• Legally eligible to work in the U.S. on an ongoing basis
• BS or MS in Computer Science, a related field, or equivalent industry experience

Strong Plus:

• Experience with MLflow or similar experiment tracking platforms
• Familiarity with CI-integrated evaluation pipelines
• Experience with multi-agent orchestration frameworks
• Prior work in an Applied AI or LLMOps function within a product company

What Success Looks Like:

• Delivered measurable, validated quality improvement on at least one SmartAssist agent across our defined quality dimensions
• Expanded evaluation coverage to close the most significant blind spots in our current framework
• Established a repeatable quality improvement methodology the broader team can apply