Introduction
This article outlines the role and responsibilities for a full-stack engineer focused on AI-enabled platforms. It details building and maintaining scalable front-end and back-end components, integrating and fine-tuning LLMs, developing conversational AI and analytics, optimizing APIs and data pipelines, and participating in testing, code reviews, and CI/CD-driven deployment workflows.
Role & Core Responsibilities
As a core contributor, you will build and maintain scalable front-end and back-end components using modern frameworks (React/Next.js + Node.js/NestJS or similar). Responsibilities are practical and hands-on, centered on delivering reliable, modular systems that support AI features and real-time interfaces.
- Build and maintain components: Implement and upkeep UI and server-side modules with a focus on scalability, modularity, and maintainability across React/Next.js and Node.js/NestJS stacks.
- AI integration: Collaborate closely with the AI engineering team to integrate and fine-tune LLMs (OpenAI, HuggingFace, LangChain, etc.) so model behavior aligns with product needs.
- Conversational AI & workflows: Develop conversational AI modules and simulation workflows that drive interactions and test cases within the platform.
- Real-time analytics: Create and maintain real-time analytics dashboards to surface metrics and operational insights for AI-driven features.
- Performance optimization: Optimize APIs and data pipelines for performance and scalability, ensuring the system supports production loads and efficient data flows.
- Engineering practices: Write clean, modular code; participate in code reviews and sprint planning to maintain code quality and aligned delivery.
- Quality and deployment: Contribute to testing, debugging, and deployment processes using Git and CI/CD tools to ensure reliable releases and rapid iteration.
Skills: Must-Have and Good-to-Have
Success in this role depends on a combination of core technical skills, AI familiarity, and collaborative practices. The following lists outline the essential and helpful proficiencies.
- Must-Have Skills
- JavaScript/TypeScript & frameworks: Strong knowledge of JavaScript/TypeScript with exposure to React and Node.js for building full-stack applications.
- APIs & architecture: Understanding of REST APIs and microservices architecture to design interoperable, scalable services.
- LLM familiarity: Familiarity with LLM technologies like GPT, LangChain, or RAG-based systems to collaborate on model integration and tuning.
- Practical experience: Hands-on experience (academic/projects) in building full-stack applications demonstrating applied knowledge.
- AI curiosity: Curious and self-driven with a good grasp of AI/ML basics, prompt engineering, and the evolving AI landscape to iterate effectively on model-driven features.
- Good to Have
- Cloud services: Experience with cloud services (AWS, GCP, Vercel, etc.) to support deployment and scalable infrastructure.
- Vector databases: Exposure to vector databases (e.g., Pinecone, Weaviate) useful for retrieval-augmented approaches.
- Real-time tools: Understanding of Next.js, WebSockets, or Redis for building responsive, real-time interfaces and caching layers.
- Collaboration & DevOps: Familiarity with version control (Git), agile tools (Jira/Notion), and DevOps basics to integrate with team workflows and CI/CD.
Conclusion
This role blends full-stack development with hands-on AI integration: building scalable front-end/back-end systems, collaborating to fine-tune LLMs, developing conversational modules and real-time analytics, and ensuring performance via optimized APIs and CI/CD. Candidates should combine strong JavaScript/TypeScript and API knowledge with practical full-stack experience and a curiosity about AI, while cloud, vector DB, and real-time tool exposure are advantageous.
