Senior Fullstack AI Engineer

Responsibilities: Design, develop, and deploy end-to-end AI-powered applications using React, Python, FastAPI, and AWS services. Build responsive and scalable frontend applications using React and optionally Next.js, ensuring seamless user experiences for AI-driven workflows. Develop backend APIs and microservices using Python and FastAPI to support AI and business application requirements. Design and implement Retrieval-Augmented Generation (RAG) pipelines, including document ingestion, chunking, embedding generation, retrieval optimization, and response orchestration. Implement and integrate Model Context Protocol (MCP) servers and clients to enable secure communication between AI systems, tools, and enterprise applications. Build and maintain AI agent workflows leveraging Amazon Bedrock, foundation models, and agent orchestration frameworks. Integrate with LLM providers and Bedrock foundation models while implementing prompt engineering, tool calling, structured outputs, and reasoning workflows Design and implement vector database integrations and semantic search capabilities to support enterprise knowledge retrieval use cases. Develop real-time AI experiences using streaming APIs, Server-Sent Events (SSE), and WebSocket-based architectures Collaborate with product managers, architects, and business stakeholders to translate requirements into scalable AI solutions. Implement security, authentication, authorization, and observability best practices across AI applications and APIs. Participate in architecture reviews, code reviews, and technical design discussions while contributing to engineering best practices. Required Skills:

5+ years of software engineering experience with strong full-stack development expertise. Strong hands-on experience with React and modern JavaScript/TypeScript development. Experience with Next.js for server-side rendering and modern web application development (preferred but not mandatory) Strong proficiency in Python and backend development using FastAPI. Experience designing and implementing Retrieval-Augmented Generation (RAG) systems in production environments. Hands-on experience with Model Context Protocol (MCP), including MCP clients, servers, tools, and integrations. Strong knowledge of Amazon Bedrock and foundation model integrations. Experience working with LLMs such as Claude, Llama, Amazon Nova, Mistral, OpenAI, or similar models Experience implementing vector databases and semantic search solutions using technologies such as Pinecone, Weaviate, Chroma, or FAISS. Strong understanding of prompt engineering, tool calling, agent orchestration, and AI application architectures Experience developing REST APIs, microservices, and event-driven architectures. Familiarity with AWS services including Bedrock, Lambda, API Gateway, S3, DynamoDB, ECS/EKS, CloudWatch, IAM, and Secrets Manager. Experience with Docker, containerized deployments, and CI/CD pipelines. Strong understanding of authentication and authorization mechanisms including JWT, OAuth, and OIDC. Excellent problem-solving, communication, and collaboration skills.

Required Skills

AI/ML GenAI Angular