Role: Next.js Developer + AI/LLM’s (Onsite in Franklin, TN)

Job Title: Next.js Developer + AI/LLM's 
Location: Franklin, TN, USA Onsite
Contract: C2C/W2/1099
Visa: Any Visa 
Exp: 8 to 10 years 
Job Summary:
We are looking for a Full Stack Developer with strong experience in Next.js to build high-performance, scalable, and intelligent web interfaces. The ideal candidate has a keen eye for clean UI/UX, is comfortable working in agile teams, and is curious about integrating AI/LLM-powered experiences into the frontend.
Must-Have Skills:
  • Next.js – Deep hands-on experience with SSR, static generation, routing, and performance optimization JavaScript (ES6+)
  • Strong fundamentals, async/await, closures, promises.
  • React.js – Component-based architecture, hooks, state management (e.g., Redux, Context)
Must-Have Skills:
  • AI / LLMs
  • Familiarity with integrating frontend with LLMs (e.g., Open AI, Bedrock, Hugging Face, Lang Chain) Tailwind CSS / Styled Components
  • Modern styling techniques Angular
  • Bonus points for experience, especially in hybrid environments REST APIs
  • Comfortable consuming APIs and handling data flows Tableau integration
  • Embedding or interacting with dashboards.
  • Snowflake (basic) – Understanding of frontend integration with data APIs.
  • Scrum / Agile – Participating in sprint ceremonies and iterative development.
  • Jira – Managing tasks, bugs, and sprint planning.
Key Responsibilities:
->Design and implement responsive UIs using Next.js. Optimize performance, accessibility, and cross-browser compatibility.
->Collaborate with design, product, and backend teams to deliver end-to-end features.
->Integrate AI/LLM features into the UI (e.g., smart forms, chat assistants, auto-suggestions)
->Build reusable components and libraries.
->Participate in sprint planning, stand-ups, and reviews.
 
Preferred Qualifications: Bachelor’s degree in computer science or related field
->Strong portfolio of Next.js or AI-integrated frontend projects
 
Bonus:  Experience with vector DBs, RAG pipelines, or embedding LLMs into apps
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