Associate Developer(Full Stack with AI)

Requirements

Required Skills & Qualifications:

• Proven Experience:
  
  Minimum of 1-2 years in full-stack web development, with hands-on experience in both front-end and back-end technologies.
  
• Tech Stack:
  
  Strong proficiency in modern web development technologies such as JavaScript (React, Angular, Node.js, etc.), HTML5, CSS3, TypeScript, and server-side frameworks like Express.js, Django, or Ruby on Rails.
  
• AI & Automation Tools:
  
  Strong experience with AI/ML models and familiar with automation tools such as n8n to configure workflows and applications seamlessly.
  
• Agentic AI & OpenClaw Experience:
  
  Experience with AI platforms like OpenClaw or similar Agentic AI tools. Ability to add new skills to AI agents, integrate multiple systems, and create job schedules.
  
• Database Proficiency:
  
  Strong knowledge of relational and NoSQL databases (e.g., MongoDB, PostgreSQL, MySQL), including querying, schema design, and performance optimization.
  
• API Development:
  
  Solid experience in designing and developing RESTful and GraphQL APIs.
  
• DevOps & CI/CD:
  
  Familiarity with DevOps practices and tools like Docker, Kubernetes, Jenkins, or similar platforms to streamline deployment and automation pipelines.
  
• Version Control & Collaboration Tools:
  
  Proficient in version control using Git and collaboration tools such as Jira, Slack, and others.
  
• Problem-Solving:
  
  Strong problem-solving skills, with the ability to work on complex systems and debug challenging issues.
  
• Excellent Communication:
  
  Ability to collaborate effectively with both technical and non-technical teams. Strong written and verbal communication skills.
  

Desired Skills (Optional but a Plus):

• Cloud Platforms:
  
  Experience with cloud platforms such as AWS, Azure, or Google Cloud for application deployment and scaling.
  
• Experience with Microservices:
  
  Familiarity with designing, implementing, and managing microservices architecture.
  
• AI Model Optimization:
  
  Experience in optimizing AI models and working with platforms for AI deployment like TensorFlow, PyTorch, or similar.
  
• Data Engineering:
  
  Knowledge of data pipelines, ETL processes, and working with large datasets for training AI models.