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.

Experience with agentic software development methodologies using AI agents to accelerate development workflows, including coding, testing, debugging, and code review.