AI Fullstack engineer

Leverage AI coding assistants (e.g., GitHub Copilot, agentic IDEs) to accelerate delivery while maintaining quality. Implement UI/UX experiences for AI-enabled features (explanations, feedback loops, human-in-the-loop controls). Apply secure SDLC practices: code reviews, testing, dependency management, and vulnerability remediation. Partner with architects and platform teams to align to standards and reuse shared components. 5+ years building production software (backend and/or frontend) in Java/Python/.NET ecosystems. Experience with web frameworks and modern UI (React or similar) and REST API development. Working knowledge of CI/CD, Git workflows, and automated testing. Comfort integrating with ML services, LLM/agent runtimes, or data platforms via APIs.

Strong problem-solving skills and ability to deliver iteratively in an agile environment. Experience building internal tools or copilots with prompt engineering and tool/function calling. Experience with observability for AI features (quality metrics, prompt/model versioning). UX experience designing AI interactions and feedback capture.Fullstack Integration, LLM Orchestration, and User Experience. Mandatory Skills (The "Must-Haves") LLM Orchestration: Mastery of frameworks like LangChain or LangGraph to manage multi-turn agentic workflows. GenAI Implementation: Practical experience with RAG (Retrieval-Augmented Generation) using vector databases like FAISS, Pinecone, or Azure Cognitive Search. API & Microservices: Advanced development of services that orchestrate model inference and tool integrations using FastAPI or Node.js. AI Coding Assistants: Effective use of GitHub Copilot or agentic IDEs to accelerate delivery without sacrificing code quality. UI/UX for AI: Ability to build "human-in-the-loop" controls and feedback loops into the frontend. Good-to-Have Skills Vector Embeddings: Deep understanding of embedding models (e.g., OpenAI Ada) and chunking strategies. Containerization: Proficiency in Docker and Kubernetes (AKS/EKS) for sustaining high throughput (1K+ RPS). Observability: Experience with OpenTelemetry or Azure Monitor to track agent reliability and response accuracy.