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You will work closely with our Agent Engineering and AI Platform teams, embedded in a team that has already shipped evaluation infrastructure on Databricks/MLflow and is building toward a mature Agent Development Lifecycle (ADLC).

You Will:

• Own agent quality end-to-end: diagnosis, improvement, and validation across SmartAssist's orchestrator and subagents
• Identify failure modes across quality dimensions factual accuracy, completeness, tone, actionability, and latency and prioritize what to fix
• Drive quality improvements through prompt engineering, context engineering, and RAG retrieval tuning
• Extend and mature our evaluation framework: scorers, golden datasets, regression gates, and online evaluation for production traffic
• Close the feedback loop ensure that every change has a measurable, attributable quality signal
• Collaborate with our Agent Architecture lead to distinguish quality problems that require prompt/context solutions from those that require structural fixes
• Establish repeatable methodology that scales beyond any single agent or subagent

You Have:

Required

• 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:

In your first 6 months, you will have:

• 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

Why This Role:

• You will have real ownership, not a supporting role on someone else's roadmap
• SmartAssist is shipping to real users; this work has immediate, visible impact
• You will be part of a fast-moving team that deeply values engineering rigor and actively seeks diverse perspectives

Current US Perks & Benefits: