About the Job/Role

Develop scalable applications and intelligent systems using .NET or Python.
Integrate LLMs with enterprise data and optimize AI solutions for performance, scalability, and production-grade use.

Roles and Responsibilities

Design, develop, and maintain applications in .NET (C#) or Python.
Implement RAG pipelines by integrating LLMs (OpenAI, Azure OpenAI, Hugging Face, LangChain, LlamaIndex, etc.) with enterprise and external data sources.
Develop MCP-based integrations to connect tools, APIs, and enterprise data systems with LLMs.
Build APIs and microservices for AI-powered search, summarization, and conversational AI.
Create document ingestion pipelines (PDFs, databases, SharePoint, etc.) and manage embeddings with vector databases (Pinecone, Weaviate, FAISS, Qdrant, Azure Cognitive Search, etc.).
Collaborate with AI engineers, architects, and data teams to ensure scalable deployment of RAG/MCP solutions.
Optimize application performance, security, and scalability for production-grade AI systems.
Stay updated with AI frameworks, MCP standards, and cloud AI services.

Required Skills & Experience

Minimum of 8 years of IT experience, with 1+ years in AI.
Strong hands-on experience in .NET (C#) or Python.
Solid understanding of OOP, REST APIs, and microservices architecture.
Proven experience with LLM-based applications and RAG (Retrieval-Augmented Generation) integration.
Knowledge and practical implementation of Model Context Protocol (MCP) for AI tool orchestration.
Familiarity with vector databases (FAISS, Pinecone, Weaviate, Qdrant, Azure Cognitive Search).
Hands-on experience with LangChain, LlamaIndex, Hugging Face Transformers, or similar AI libraries.
Strong problem-solving and cross-functional collaboration skills.

Good to Have

Experience with containerization (Docker, Kubernetes).
Experience with cloud AI services (Azure, AWS, GCP) for deployment and scaling.
Exposure to SQL/NoSQL databases for structured and unstructured data.
Prior experience in chatbot development, enterprise search, or knowledge management systems.
Understanding of MLOps practices for AI model deployment and monitoring.

Top Skills

.NET (C#) | Python | OpenAI | Azure OpenAI | LangChain | LlamaIndex | Hugging Face | RAG | MCP
Vector Databases: FAISS, Pinecone, Weaviate, Qdrant, Azure Cognitive Search
SQL | NoSQL | Docker | Kubernetes | Cloud AI Services (Azure, AWS, GCP)

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