Sr. Software Engineer II - AI Engineering (Remote Eligible)

For over 20 years, Smartsheet has empowered teams to manage work seamlessly and scale solutions smarter. Now, in our most ambitious chapter yet, we are uniting human teams with AI agents. By orchestrating the work agents do best, automating manual tasks and uncovering insights at scale, we create the space for people to focus on what truly matters: judgment, creativity, and big thinking. That is magic at work, and it’s what we show up for every day.

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...