1. Full-Stack Development & Implementation:
- Design, implement, test, and deploy robust backend components for the agentic AI platform (APIs, services, orchestration, data persistence) using languages like Python, Typescript, Java, etc. (Python highly relevant).
- Design, implement, test, and deploy user-facing features and internal tools using modern frontend technologies, including JavaScript, TypeScript, React, HTML, and CSS.
- Develop core libraries, SDKs, and components across the stack to enable efficient development and deployment of AI agents and associated tooling.
- Write high-quality, maintainable, and well-tested code for both backend and frontend systems.
- Implement and improve monitoring, logging, and alerting across the full stack.
- Participate actively in code reviews for both frontend and backend code.
2. Technical Design & Contribution:
- Contribute significantly to technical design and architecture discussions, considering both backend and frontend implications for new features and system improvements.
- Collaborate with principal engineers, product managers, UX/UI designers, and researchers to refine requirements and translate them into effective full-stack technical solutions.
- Evaluate and prototype new technologies, algorithms, and frameworks relevant to agentic AI, platform infrastructure, and frontend development.
- Take ownership of the technical implementation for specific features or components spanning the full stack.
3. Collaboration & Knowledge Sharing:
- Work closely with teammates and cross-functional partners (Product, Design, Research, other Engineering teams) to deliver cohesive user experiences and robust backend capabilities.
- Share knowledge, document designs and processes effectively for both backend and frontend aspects.
- May mentor junior engineers on full-stack development practices.
4. Operational Excellence:
- Troubleshoot and debug complex issues in production environments, potentially spanning frontend, backend, and infrastructure layers.
- Participate in addressing issues across the stack.
- Contribute to improving the scalability, performance, security, usability, and cost-effectiveness of the platform and its interfaces.
What You Should Bring
- Bachelors degree (or equivalent practical experience) in Computer Science, Engineering, or a related field.
- 2+ years of professional full-stack software development experience.
- Strong proficiency in one or more backend programming languages such as Python, Javascript/Typescript or Java (Python highly relevant for the AI domain).
- Strong proficiency in frontend technologies including JavaScript, TypeScript, HTML, CSS, and modern frontend frameworks (specifically React).
- Solid understanding of computer science fundamentals (data structures, algorithms, operating systems, networking).
- Experience building, deploying, and operating backend services and APIs in a cloud environment (AWS, GCP, or Azure).
- Experience building and deploying responsive and performant user interfaces.
- Experience with system design and architecture, with the ability to contribute to complex design discussions covering the full stack.
- Interest in or practical experience with AI/ML concepts, particularly working with or integrating Large Language Models (LLMs).
- Experience with software development best practices (testing frameworks for backend and frontend, CI/CD, monitoring, code reviews).
- Strong problem-solving skills and attention to detail.
- Good communication and collaboration skills, including working with designers.
Preferred Qualifications
- Experience building platform or infrastructure-as-a-service components.
- Experience building developer tools or interfaces for complex technical products (e.g., AI/ML tooling, data platforms).
- Hands-on experience with agentic AI concepts/frameworks (e.g., LangChain, LlamaIndex), vector databases (e.g., Pinecone, Weaviate), or RAG techniques.
- Experience with state management libraries (e.g., Redux, Zustand) and frontend build tools (e.g., Webpack, Vite).
- Experience with MLOps practices and tools.
- Experience with containerization (Docker) and orchestration (Kubernetes).
- Experience with infrastructure-as-code (e.g., Terraform).
- Experience optimizing the performance and scalability of both backend distributed systems and frontend applications.
