Senior Full Stack Software Engineers

About the Role

Senior Full Stack Software Engineers

Key Responsibilities

• Design, develop, and maintain full-stack applications using

  ASP.NET Core

  ,

  C#

  , and

  React.js

  .
• Build and integrate

  RESTful APIs

  to support seamless communication between front-end and back-end systems.
• Work with

  MS SQL Server

  for database design, query optimization, and performance tuning.
• Collaborate with DevOps teams to implement

  CI/CD pipelines

  and containerized deployments.
• Deploy and orchestrate microservices using

  Docker

  and

  EKS

  on Azure.
• Ensure adherence to best practices in software development, including code quality, testing, security, and scalability.
  
• Participate in architectural discussions, mentor junior engineers, and conduct code reviews.
  
• Work within Agile teams to deliver high-quality solutions on time.
  

Required Skills & Experience

• 68 years of professional experience in full-stack software development.
  
• Strong expertise in

  ASP.NET Core

  ,

  C#

  ,

  React.js

  , and modern

  JavaScript frameworks

  .
• Proven experience working with

  RESTful APIs

  and

  MS SQL Server

  .
• Hands-on experience with

  Azure

  cloud services.
• Practical knowledge of

  CI/CD pipelines

  ,

  containerization

  ,

  Docker

  , and

  EKS

  .
• Strong analytical and problem-solving skills with attention to detail.
  
• Excellent communication and collaboration abilities.
  

Nice to Have

• Experience with

  microservices architecture

  and distributed systems.
• Familiarity with

  Infrastructure as Code (IaC)

  tools such as Terraform.
• Exposure to monitoring and logging tools (e.g., Prometheus, Grafana, ELK Stack).
  
• Knowledge of testing frameworks and automation practices.
  

• AI/ML Exposure (Optional):

• Experience integrating AI services (e.g., Azure Cognitive Services, OpenAI, ML.NET).
  
• Knowledge of AI-based feature development such as chatbots, recommendation systems, or predictive analytics.
  
• Familiarity with data pipelines or model deployment in production environments.