Key Responsibilities
- Design and Develop Full-Stack Features:
- Build and enhance core modules across frontend (React/TypeScript) and backend ) aligned with the product roadmap.
- Integrate LLM & RAG Workflows:
- Implement prompt pipelines, embedding models, and hybrid retrieval (Qdrant / Oracle 23AI vector search) for contextual AI responses.
- Backend Services & APIs:
- Develop scalable REST/GraphQL APIs, implement proxy layers for database and LLM access, and ensure robust request orchestration.
- Database & Data Services:
Work with
MongoDB, Oracle
, and
Qdrant
using various database integration patterns.
- Frontend Engineering:
- Develop modular, high-performance UI using
React + Vite + Tailwind CSS
, integrated with role-based access.
- Implement dynamic dashboards, charts, mind maps, and prompt management UIs.
- AI Agent Integration:
- Collaborate on MCP (Model Context Protocol) plugin integration for connecting AI agents to enterprise systems like CRM, Billing, and Product Catalog.
- DevOps & Deployment:
- Support containerization (Docker/Kubernetes), CI/CD (Azure DevOps), environment management, and on-prem/cloud deployments.
- Performance Optimization & Security:
- Optimize database queries, LLM calls, and real-time communication layers while ensuring secure handling of enterprise data.
Required Technical Skills
Frontend Stack:
- React 18+, Vite, TypeScript, Tailwind CSS
- State management: Redux / Zustand
- Charting and visualization: Recharts, D3, Mermaid
- Responsive and dark-theme UI implementation
Backend Stack:
- 18+, Express / Fastify
- REST & GraphQL API design
- Proxy architecture and middleware design
- Authentication (JWT/OAuth2, Azure AD)
- File handling, PDF/Doc generation, and AI pipeline integration
Databases:
- MongoDB / Mongoose
- Qdrant / Oracle 23AI (Vector Search, Hybrid + Rerank retrieval)
AI & Integration Layer:
- OpenAI / Azure OpenAI APIs
- Gemini and LLaMA model adapters
- Embedding and RAG architecture
- Model Context Protocol (MCP)
- Knowledge indexing and retrieval optimization
DevOps & Tools:
- Docker, Kubernetes, Nginx ingress routing
- CI/CD using Azure DevOps / GitHub Actions
- Git branching, versioning, and backup strategies
- Environment management (.env, secrets, configs)
Preferred Experience
- 3–5 years of hands-on full-stack development experience
- Experience in
AI-powered enterprise systems
or
LLM-based applications - Familiarity with
telecom BSS/OSS domains
(CRM, Billing, Product Catalog, Order Management) is an added advantage - Prior exposure to
MCP
,
RAG
, or
Vector DB
systems will be preferred.
- Comfortable working in a
modular micro-service architecture - Strong debugging, documentation, and collaboration skills
Soft Skills
- Excellent problem-solving and analytical mindset
- Strong communication and documentation habits
- Ability to work independently in a fast-paced, evolving environment
- Collaborative mindset to work with architects, AI engineers, and UI designers