Full Stack AI Engineer

About the role

You will work directly with the founders and influence product direction, technical architecture, hiring, and how Scispot builds. You will be trusted with real production ownership early. There is no long waiting period before your work matters. You will hear directly from scientists when something you built saves them time—or fails to solve their problem.

Responsibilities

• Own major product areas from first customer conversation to production. You will define the problem, choose the architecture, build the interface and backend, deploy it, measure its impact, and keep improving it.
• Build the GLUE and API layers that reliably move data from lab instruments, external systems, and scientific files into Scispot.
• Let scientists query the full journey of a sample—from batch to run to assay—using natural language, graph-backed data, and grounded retrieval.
• Build AI agents that can take useful actions inside lab workflows, not merely produce text.
• Improve recommendation, search, memory, and retrieval systems across OpenSearch, vector databases, graph databases, and LLM pipelines.
• Turn one-off lab instrument integrations into reusable connectors, SDKs, templates, and eventually a self-serve connector ecosystem.
• Build polished workflows across React and TypeScript, Java Spring Boot and Python services, APIs, queues, data stores, and AWS infrastructure.
• Improve EKS infrastructure, automated tests, telemetry, incident response, and system reliability as Scispot grows.
• Speak with scientists and lab operators regularly. Learn how their work actually happens. Then ship tools that remove real bottlenecks.
• Turn incomplete requirements and messy scientific workflows into simple, reliable software without waiting for a perfect PRD.

Requirements

• 6+ years of experience in engineering, full stack.
• Experience with AWS services (ECS, Lambda, RDS, S3, CloudWatch, etc.).
• Experience with Next.js.
• Experience with Spring Boot and/or FastAPI.
• Exposure to Generative AI, LLMs, RAG systems, AI agents, or AI-powered products.
• Experience with Docker, Kubernetes, CI/CD pipelines, and infrastructure automation.
• Familiarity with compliance, governance, security, risk, or regulated industries.
• Experience working in an early-stage startup environment.

Qualifications

• Strong product judgment. You know when to ship a simple version and when reliability, security, or data integrity requires deeper work.
• Comfortable moving between a React interface, backend services, APIs, databases, queues, cloud infrastructure, and AI systems.
• Enjoy speaking with users and believe customer discovery is part of engineering.
• Take responsibility for outcomes, including bugs, failed assumptions, production issues, and adoption after launch.
• Initiative and resourcefulness shown in previous projects or open-source contributions.
• Experience with Generative AI, LLMs, RAG systems, AI agents, or AI-powered products.
• Experience with Docker, Kubernetes, CI/CD pipelines, and infrastructure automation.
• Familiarity with compliance, governance, security, risk, or regulated industries.
• Experience working in an early-stage startup environment.