Role Overview
We are seeking a hands-on AI Full Stack Developer to build and deliver end-to-end AI-powered applications that integrate LLM/GenAI capabilities into real-world business workflows.
This role is focused on AI-driven product development, combining backend services, frontend experiences, APIs, and intelligent workflows to create scalable and user-centric applications (e.g., chatbots, copilots, AI assistants).
Key Responsibilities
Build and deploy end-to-end AI applications (e.g., chatbot/copilot platforms) with UI, APIs, and backend services
Design and develop REST APIs and services for LLM inference, prompt orchestration, and agent workflows
Implement AI-driven user experiences, including:
o Explainability features
o Feedback loops
o Human-in-the-loop (HITL) workflows
Develop and manage prompt flows, tool/function calling, and agent orchestration
Integrate AI/ML services with enterprise systems and external APIs
Build React (or similar) based frontends to enable intuitive interaction with AI systems
Ensure secure SDLC practices including code reviews, testing, dependency management, and vulnerability fixes
Collaborate with architecture, platform, and product teams to align with enterprise standards
Leverage AI coding tools (e.g., Copilot, agentic IDEs) to improve developer productivity
Required Skills (Must-Have)
5+ years of full-stack software development experience
Strong programming skills in Python (preferred) / Java / .NET
Hands-on experience building LLM/GenAI-powered applications (production or strong POCs)
Strong experience with:
o REST API development
o Backend service design and integration
o Modern frontend frameworks (React preferred)
Practical experience with:
o LangChain / LangGraph / agent frameworks
o Prompt engineering and prompt workflows
o Tool/function calling and API orchestration
Strong understanding of:
o AI system design and application architecture
o LLM integration patterns (RAG, agents, copilots)
Experience with CI/CD, Git workflows, and automated testing
Nice-to-Have (Highly Preferred)
Experience building AI copilots, chatbots, or internal AI assistants
Knowledge of AI observability and evaluation:
o Prompt/version tracking
o Model evaluation metrics
Experience designing AI interaction UX (explainability, feedback, HITL)
Exposure to LLM runtimes, embeddings, and vector databases
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