Fulltime Remote Fullstack Javascript/Typescript/NodeJS

Key outcomes Deliver High-Quality Code: Consistently submit well-documented, maintainable code with high test coverage (>80%) through PRs, following team standards and best practices. Apply Strong Fundamentals: Design and build features applying solid software architecture (e.g., SOLID, clean architecture), efficient database design, and API best practices. Leverage AI Tools Effectively: Use AI coding assistants to speed up development (e.g., boilerplate, test writing, debugging) without sacrificing code quality. Share effective prompts and techniques Contribute to System Stability: Maintain production rollback rates below 2% by enforcing thorough testing, leveraging CI/CD, and following DevOps best practices. Engage in Collaborative Development: Actively join code reviews, provide constructive feedback, and respond positively to feedback to boost team velocity and knowledge sharing. Requirements Experience: 5+ years as a Fullstack Engineer, AI Specialist, or similar, with a strong track record of deploying scalable, AI-integrated applications. Frontend & Backend Skills: Proficient in VueJS/React and NodeJS (TypeScript), with experience building modular, reusable, and resilient microservices. Database & Architecture: Skilled in MongoDB/Supabase, REST/RPC, and designing systems aligned with Clean/Hexagonal architecture, SOLID principles, ACID compliance, and idempotency. AI Expertise: Experienced in AI ecosystems like LangChain and LlamaIndex, with hands-on skills in prompt engineering, RAG, and curiosity about specialized hardware like Groq. Quality Commitment: Dedicated to high testing standards, secure coding, and detailed documentation for AI pipelines to ensure reliable performance. Communication: Strong communicator, adept at collaborating with Agile teams and connecting product, UX, data science, and engineering to drive AI initiatives. Cloud & DevOps: Knowledgeable in AWS/GCP, serverless/PaaS solutions, and Kubernetes deployments, with a focus on MLOps and AI model lifecycle best practices. Versatility: Comfortable working independently or collaboratively, pushing projects from concept to production while inspiring your team.