Fullstack-AI Engineer

Job Description

Looking for strong Full Stack Engineers with hands-on experience in Angular-based frontend development and a solid understanding of AI-driven systems and MCP-based architectures to work on a scalable LLM-powered conversational platform.

This role is ideal for engineers who have hands on experience in building end-to-end systems — from real-time conversational UIs to distributed backend services integrating LLMs and agent frameworks.

Key Responsibilities

• Build and maintain LLM driven enterprise level applications in real production environment.
• Design and develop MCP tools using Python, FastAPI, or similar frameworks

• Maintain multiple MCP services and LLM integrations.

• Develop Angular-based conversational user interfaces with real-time updates.

• Design scalable architectures capable of supporting high concurrency.
• Implement monitoring, logging, and tracing for AI workflows.
• Continuously optimize latency, cost, and response quality.
• Implement and support multi-agent architectures with context sharing and orchestration
• Implement WebSocket-based communication for streaming AI responses
• Use Redis for caching, session management, and conversation memory
• Maintain scalable services for conversation state, workflow, and memory handling

• Strong understanding of API contracts and schema-driven development.

• Understanding of Prompt engineering, Structured outputs,Tool calling patterns,Model limitations and failure modes.

• Good Understanding of Agent orchestration patterns,Role-based agents,Task decomposition,Coordination and fallback mechanisms.

Technical Skills

• Frontend:
• Angular / React
• RxJS and reactive programming
• Real-time UI updates using WebSockets
• Backend:
• Python
• FastAPI / async services
• API and middleware development
• AI & Platform:
• LLM integrations and tool calling
• MCP / FastMCP
• LangChain, LangGraph
• Multi-agent architectures
• Systems & Infrastructure:
• Distributed systems and scalability concepts
• WebSockets, event-driven systems
• Redis (caching, session, memory)
• Databases: Cosmos DB, MongoDB
• Vector databases: Pinecone or similar
• Cloud & DevOps:
• Azure, Azure AI Foundry
• Azure GitHub (PRs, branching, CI/CD pipelines)