Fullstack Developer (AI)
Мэтч & Сопровод
Для мэтча с этой вакансией нужен Plus
Описание вакансии
TL;DR
Fullstack Developer (AI): Building and scaling the Shadow AI coordination layer for marketing teams with an accent on integrating LLMs, RAG pipelines, and distributed services. Focus on designing end-to-end features, ensuring seamless API-to-UI data flow, and driving technical architecture within a high-growth consumer brand environment.
Location: Must be based in New York, Florida, New Jersey, or Texas
Salary: $120,000
Company
is a performance marketing agency and product company empowering consumer brands with AI-driven workflow automation and agent management tools.
What you will do
- Architect and implement full-stack features from database schema to React components.
- Build and maintain RESTful/GraphQL APIs and distributed services in GCP.
- Integrate, prompt, and debug LLMs and generative AI tools, including RAG pipelines.
- Ensure seamless interoperability between front-end and back-end systems.
- Collaborate with product, research, and design teams to ship production-grade code.
- Mentor junior engineers through code reviews and architecture guidance.
Requirements
- 5+ years of professional software engineering experience with end-to-end ownership.
- Deep expertise in Python, Node.js, React/TypeScript, and PostgreSQL.
- Hands-on experience with GCP, containerization (Docker/K8s), and high-traffic systems.
- Proven experience integrating AI/ML models (LLMs, NLP, RAG) into production apps.
- Must be based in New York, Florida, New Jersey, or Texas.
- Strong product mindset with a focus on UX, performance, and business impact.
Nice to have
- Experience with multi-step or agentic AI workflows.
- Background in AI infrastructure or tooling companies.
- Contributions to open-source AI/ML projects.
Culture & Benefits
- Competitive salary with performance-based compensation.
- Top-tier health, vision, and dental insurance (US).
- Regular team off-sites and hack weeks.
- Opportunity to work with real-world marketing data at scale.
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