Full-Stack Engineer_Hybrid; NYC

Full-Stack Engineer

We are looking for a Full-Stack Engineer to join a fast‑moving applied AI and data analytics company building the intelligence layer for enterprise decision‑making. This is a full‑ownership role on a small, high‑trust team: you will take features from idea to production, work directly with enterprise clients, and have real influence on architecture, product direction, and culture. If you have built new things on small teams and want to own what you ship — not just contribute to it — this role is for you.

• Schedule:
  
  Full‑time
• Location:
  
  Hybrid (NYC)
• Type of collaboration:
  Full‑time employment

The platform connects an organization’s entire data landscape — internal systems, social media trends, industry reports, consumer behavior signals — into a single coherent intelligence layer. It surfaces insights and automates workflows that used to take analysts weeks. The core thesis: most enterprise data is disconnected, and massive value is lost in that gap. The platform flips sentiment from a lagging indicator into a leading indicator, enabling brands to make decisions months faster than legacy research tools allow.

The engineering team operates on a DRI (Directly Responsible Individual) model — you own major product features from conception through launch, not just contribute to them. You will build across every layer of the stack: backend services that process enterprise data at scale and frontend interfaces that make intelligence accessible and actionable. You will collaborate with ML engineers to bring AI‑driven features to production and work directly with enterprise customers to understand real‑world needs and refine the product.

• 5+ years of professional engineering experience with meaningful work across both frontend and backend
• Proficiency in Type Script/React and at least one of:
  Go, Python
• Strong product instincts — you think about the user, not just the code
• Experience with cloud infrastructure (AWS preferred) and modern deployment practices
• Experience building new things on small teams, not maintaining legacy systems at scale

• Experience with agentic AI systems, LLM integrations, or RAG architectures
• Background in enterprise SaaS, retail technology, or data‑intensive products
• Familiarity with data visualization, real‑time systems, or streaming architectures
• Contributions to developer tooling, CI/CD, or infrastructure automation
• Graph database experience

• Build and ship features end-to-end — backend services in Go/Python and frontend experiences in React/Type Script
• Design APIs, data models, and service architectures that support agentic AI capabilities
• Create intuitive interfaces that translate complex enterprise data into clear, actionable workflows
• Collaborate with ML engineers to bring AI‑driven features to production
• Own features through the full lifecycle: scoping, architecture, implementation, testing, deployment, and iteration
• Work directly with enterprise customers and stakeholders to understand real‑world needs and refine the product
• Contribute to infrastructure, tooling, and developer experience as the engineering team scales