Fullstack Developer

Responsibilities:

• Build and develop technical work outputs for each work package.
• Implement user interfaces and integration layers for AI agents.
• Develop APIs and data pipelines connecting AI systems to operational platforms.
• Build monitoring and observability solutions for AI applications.
• Implement DevOps automation and CI/CD pipelines.
• Support SDLC automation initiatives and tooling integration.

Requirements:

• Primary skills: Full stack + Frontend + Backend + End-to-end App Dev + AWS serverless + CI/CD + Infra as Code + Devops + App unit testing + Python.
• Strong experience with modern web frameworks (React) and backend development (Python, Node.js ).
• Proficiency in API development (REST, GraphQL) and microservices architectures.
• Knowledge of AWS services (Lambda, ECS, API Gateway, CloudFormation, CDK).
• Experience with DevOps tools and CI/CD pipelines (Bonus).
• Familiarity with AI/ML application integration patterns.
• Database(MS SQL, postgres, cloud DB, etc. ).
• Some proficiency in Gen AI and AI tools is a plus.
• Software development experience designing and building full-stack applications, with hands-on work across frontend (e. g., React, Next.js, Angular, Vue) and backend (e. g., APIs, microservices, data layers).
• Hands-on development with modern programming languages (e. g., Python preferred, FastAPI, TypeScript, JavaScript, Kotlin ).
• Cloud architecture and solution implementation experience on AWS or comparable cloud platforms.
• Experience with Infrastructure as Code (e. g., Terraform preferred, AWS CDK, CloudFormation), CI/CD pipelines and DevOps practices.
• Background in asynchronous API design and implementation (REST, GraphQL, gRPC) and identity protocols (OpenID Connect, OAuth 2.0).
• Experience with distributed systems, event-driven architectures, and microservices patterns at scale.
• Familiarity with observability and monitoring practices (e. g., OpenTelemetry, CloudWatch, Datadog, AppInsight, Log Analytics) with a focus on operational excellence.
• Unit testing framework - e. g pytest, NUnit. Integration testing end-to-end. (backend testing).
• Application security framework (e. g., Blackbox).
• Role-based authentication and authorisation: JWT token, Entra ID, Active Directory.
• DB optimisation, session management.
• Experience building applications that integrate generative AI or large language model capabilities (e. g., RAG, chat interfaces, agent workflows, LLM-powered features).
• Experience using AI-assisted development tools as part of the software development workflow (e. g., GitHub Copilot).
• Experience with AI agent architectures, orchestration patterns, and emerging protocols (e. g., MCP, A2A), with hands-on experience building multi-agent systems.