Senior AI Software Engineer

What You'll Do

• Design, implement, and ship AI-driven features and systems into production environments
• Lead technical decision-making for AI architecture, data modeling, and system integration within your domain
• Partner closely with Product, Engineering, and other stakeholders to translate business needs into scalable technical solutions
• Identify and address reliability, scalability, performance, and observability challenges in AI systems
• Implement and advocate for best practices for applied AI engineering, including agent evaluation, interpretability, data layer design, and monitoring
• Mentor and guide other engineers, raising the bar on AI engineering quality and decision-making
• Provide input into technical strategy and roadmap discussions related to AI capabilities and platform evolution

What You'll Build

• Agentic system design: orchestration, tool use, state/memory, multi-step workflows
• Evals + quality: harnesses, regression tests for prompts/agents, online/offline metrics, iteration loops
• Guardrails: hallucination mitigation, grounding, HITL review, safe degradation paths
• Production operations: cost/latency tradeoffs, caching, rate-limits, fallbacks, observability, incident response
• Retrieval + data foundations: RAG choices, indexing, freshness, data quality constraints, data layer design

Qualifications & Experience

• Equivalent of 5+ years of professional software engineering experience, with significant focus on applied AI
• Proven experience deploying LLM-powered systems/applications in production environments
• Strong proficiency in Python, and experience building production LLM applications (agents/tool use, retrieval, evals, monitoring)
• Experience working with large datasets and navigating real-world data quality challenges
• Strong system design, problem-solving, and architectural decision-making skills
• Experience working with agent architectures, tool interfaces, and orchestration patterns
• Desire to continually follow and experiment with cutting-edge AI technologies and tooling for use across all phases of the software development lifecycle (i.e., during design, analysis, coding, deployment, QA, etc.)
• Strong background in modern cloud-based architectures

Nice to Have

• Experience applying AI solutions in a B2B SaaS environment
• Experience interacting with data from Marketing Automation Platforms (MAPs), Customer Resource Management Systems (CRMs), and Customer Data Platforms (CDPs)
• Experience deploying AWS infrastructure, and production use of Terraform, EKS, Postgres, MySQL, Datadog, and GitLab

What We Value

• We care about what you've built, not where you learned it. If you've shipped strong software, led technical decisions under ambiguity, contributed to open source, written thoughtful technical work, or built impressive projects with modern LLM tooling - put that front and center. Show us what you've built, what problems you've wrestled with, and how you reason about tradeoffs in systems that combine LLMs with real-world constraints.