Staff Software Engineer, Customer-Facing Applied AI
New
A
AddeparInvestment technology
Remote, USAFull-TimeStaff
Salary$158,000 — $248,000 USD
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Job Details
- Experience
- 6+ years of professional software engineering experience
- Required Skills
- PythonJava
Requirements
- Have 6+ years of professional software engineering experience.
- Have professional experience with Python.
- Have deep experience building and operating production AI/LLM-powered systems, including agent architectures, multi-model orchestration, and evaluation at scale.
- Have designed technical systems that other engineers build on, with a focus on platforms and patterns.
- Have experience with AI-assisted development tools such as Claude Code, Codex, or similar.
- Have repeatedly taken products or capabilities from zero to one.
- Have experience working directly with customers or external stakeholders to scope and deliver technical work.
- Be able to lead technical direction across teams without formal authority.
- Have a BS/MS in Computer Science, Mathematics, or another quantitative field, or equivalent experience.
- Nice to have: experience with multi-service AI platform architectures, including model serving, agent orchestration, or evaluation infrastructure.
- Nice to have: experience with additional backend languages such as Java, financial services domain knowledge, production observability, SaaS operations at scale, or developer experience and internal platform work.
Responsibilities
- Set technical direction for AI engineering, including agent architecture patterns, evaluation methodology, deployment, and monitoring.
- Design shared agent frameworks, tool integrations, and evaluation infrastructure.
- Work directly with clients on complex engagements to identify problem domains and design AI capabilities for workflows.
- Make production-readiness decisions that balance speed and reliability.
- Drive the handoff from prototypes to products by working with core engineering teams to generalize client-specific capabilities.
- Lead production quality for AI systems by designing observability, establishing SLOs, and building operational practices.
- Mentor and grow AI and full stack engineers, setting the technical and cultural bar for the team.
- Translate client engagement findings into product roadmap priorities.
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