TalentAQ

TalentAQ

AI Full Stack Developer

EngineeringFull Time1-2 years

Required Skills
38 skills

Python
JavaScript
TypeScript
React.js
Angular
Vue.js
Node.js
Express.js
Django
FastAPI
MySQL
PostgreSQL
MongoDB
Redis
TensorFlow
PyTorch
Scikit-learn
Hugging Face
RESTful API
GraphQL API
Docker
Git
CI/CD
AWS
GCP
Azure
LangChain
RAG pipelines
LLMs
OpenAI
GPT
Pinecone
Weaviate
FAISS
MLOps
Figma
Tailwind CSS
Material UI

Job Description

<p>Motivated AI Full Stack Developer with 1-2 years of experience to join our growing team. The ideal candidate should be comfortable working across both front-end and back-end development, with exposure to AI/ML frameworks, and be eager to build scalable AI-powered web applications.</p><ul><li>Design, develop, and maintain end-to-end AI-driven applications (frontend + backend).</li><li>Integrate AI/ML models into production-ready applications (REST APIs, microservices, or web apps).</li><li>Work with databases (SQL/NoSQL) for storing and retrieving structured/unstructured data.</li><li>Develop front-end UIs using React, Angular, or Vue, ensuring smooth user experience.</li><li>Write clean, testable, and scalable backend services (Node.js, Python, Django, or FastAPI).</li><li>Collaborate with data scientists to deploy ML/DL models into production.</li><li>Use cloud platforms (AWS, GCP, Azure) for deploying and scaling applications.</li><li>Participate in code reviews, debugging, and performance optimization.</li><li>Stay updated with the latest AI, web, and cloud technologies.</li></ul><ul><li>Programming: Proficiency in Python and JavaScript/TypeScript.</li><li>Frontend: React.js, Angular, or Vue.js.</li><li>Backend: Node.js, Express.js, Django, or FastAPI.</li><li>Databases: MySQL, PostgreSQL, MongoDB, or Redis.</li><li>AI/ML Exposure: Familiarity with TensorFlow, PyTorch, Scikit-learn, or Hugging Face.</li><li>APIs: RESTful and GraphQL API development & integration.</li><li>DevOps/Cloud: Basic knowledge of Docker, Git, CI/CD, AWS/GCP/Azure.</li><li>Strong problem-solving skills and willingness to learn new technologies.</li></ul><ul><li>Experience with LangChain, RAG pipelines, or LLMs (OpenAI, GPT, etc.).</li><li>Knowledge of vector databases (Pinecone, Weaviate, FAISS).</li><li>Familiarity with MLOps practices for model deployment and monitoring.</li><li>UI/UX design exposure (Figma, Tailwind CSS, Material UI).</li></ul>

Motivated AI Full Stack Developer with 1-2 years of experience to join our growing team. The ideal candidate should be comfortable working across both front-end and back-end development, with exposure to AI/ML frameworks, and be eager to build scalable AI-powered web applications.

  • Design, develop, and maintain end-to-end AI-driven applications (frontend + backend).
  • Integrate AI/ML models into production-ready applications (REST APIs, microservices, or web apps).
  • Work with databases (SQL/NoSQL) for storing and retrieving structured/unstructured data.
  • Develop front-end UIs using React, Angular, or Vue, ensuring smooth user experience.
  • Write clean, testable, and scalable backend services (Node.js, Python, Django, or FastAPI).
  • Collaborate with data scientists to deploy ML/DL models into production.
  • Use cloud platforms (AWS, GCP, Azure) for deploying and scaling applications.
  • Participate in code reviews, debugging, and performance optimization.
  • Stay updated with the latest AI, web, and cloud technologies.
  • Programming: Proficiency in Python and JavaScript/TypeScript.
  • Frontend: React.js, Angular, or Vue.js.
  • Backend: Node.js, Express.js, Django, or FastAPI.
  • Databases: MySQL, PostgreSQL, MongoDB, or Redis.
  • AI/ML Exposure: Familiarity with TensorFlow, PyTorch, Scikit-learn, or Hugging Face.
  • APIs: RESTful and GraphQL API development & integration.
  • DevOps/Cloud: Basic knowledge of Docker, Git, CI/CD, AWS/GCP/Azure.
  • Strong problem-solving skills and willingness to learn new technologies.
  • Experience with LangChain, RAG pipelines, or LLMs (OpenAI, GPT, etc.).
  • Knowledge of vector databases (Pinecone, Weaviate, FAISS).
  • Familiarity with MLOps practices for model deployment and monitoring.
  • UI/UX design exposure (Figma, Tailwind CSS, Material UI).

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