Principal AI Software Engineer, GTM
New
J
JobgetherSoftware Engineering / AI
US, U.S. time zonesFull-TimePrincipal
Salary not disclosed
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Job Details
- Experience
- 8+ years of professional software engineering experience
- Required Skills
- AWSDockerPostgreSQLPythonKubernetesTypeScriptFastAPIReactLangChain
Requirements
- 8+ years of professional software engineering experience with strong fundamentals across systems, data, APIs, product surfaces, and infrastructure.
- Demonstrated ownership of production software from problem definition and discovery through launch, measurement, and iteration.
- Proven experience using AI tools and agents as an integral part of an engineering workflow.
- Strong ability to operate independently in ambiguous environments to turn unclear requirements into shipped software.
- Experience building production-quality software with attention to reliability, security, privacy, observability, and scalability.
- Experience designing or building AI-powered products, workflows, or systems.
- Familiarity with retrieval, tool use, evaluation, monitoring, orchestration, and agent workflows.
- Experience with Python, FastAPI, asynchronous workers, queue-based architectures, PostgreSQL, Redis, DynamoDB, Redshift, data pipelines, React/TypeScript, AWS, Docker, and Kubernetes.
- Familiarity with AWS Bedrock, LangChain, LangGraph, vector/semantic search, and AI coding tools like Cursor or Claude Code.
Responsibilities
- Own meaningful technical and operational problems from discovery through design, implementation, launch, measurement, and continuous improvement.
- Design and build AI-powered products and cross-cutting services that improve go-to-market workflows, decision-making, productivity, and operational effectiveness.
- Develop AI capabilities such as retrieval, contextual intelligence, evaluation frameworks, tool use, orchestration, guardrails, and agent workflows.
- Work as a hybrid product, engineering, and data builder by engaging internal users, defining success metrics, shaping workflows and user experiences, developing evaluation plans, and iterating rapidly.
- Prototype quickly using AI agents, coding tools, and automation to validate assumptions.
- Create shared AI abstractions, tooling, monitoring, logging, prompt patterns, and reusable components.
- Shape data and system architecture so AI can safely connect longitudinal signals across business systems.
- Raise standards for engineering quality, reliability, observability, security, and privacy.
- Mentor and influence other engineers by demonstrating AI-augmented engineering practices.
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