Staff Software Engineer, AI-Native Systems
New
J
JobgetherHealthcare Technology
Based in United StatesFull-TimeStaff
Salary$185,725–$264,500 USD annually
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Job Details
- Experience
- 8+ years
- Required Skills
- Node.jsPythonSQLTypeScriptCI/CDLLM
Requirements
- 8+ years of experience building and operating production software, with significant full-stack depth across TypeScript/Node.js and another programming language, preferably Python.
- Proven technical leadership as an individual contributor, including domain ownership, leading multi-engineer initiatives, and influencing decisions across team boundaries.
- Hands-on experience designing, deploying, and operating agentic systems in production, including retrieval, orchestration, tool/function calling, and evaluation.
- Strong understanding of the practical strengths and limitations of LLMs and AI agents.
- Strong cloud-native engineering fundamentals, including CI/CD, observability, production operations, and system reliability.
- Fluency with relational databases and SQL.
- Strong written and verbal communication skills, including the ability to produce compelling technical design documents.
- Comfort working remotely across functions and teams.
- Experience working in a regulated environment such as healthcare/HIPAA or financial services is strongly preferred.
Responsibilities
- Own the technical direction for a significant AI-native domain, such as agent architecture, platform abstractions, or evaluation and guardrail infrastructure.
- Serve as technical lead for a squad or cross-team initiative by decomposing ambiguous problems, sequencing delivery, removing blockers, and keeping teams focused on measurable outcomes.
- Lead architecture and design decisions, write and review design documentation, and establish clear technical ownership across complex initiatives.
- Design and build production AI agents incorporating retrieval, orchestration, policy-based routing, tool/function calling, evaluation frameworks, and lifecycle observability.
- Define standards for production-ready AI agents, including testability, rollback safety, cost controls, failure-mode management, and appropriate human-in-the-loop boundaries.
- Build and extend shared AI platform abstractions, libraries, engineering patterns, and guardrails that enable teams to integrate AI capabilities safely and consistently.
- Deliver full-stack systems using TypeScript/Node.js and Python, including services, APIs, data-processing workflows, and internal interfaces.
- Define evaluation strategies and metrics covering agent accuracy, latency, safety, reliability, and cost-effectiveness.
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