AI Application Development

• Design and develop AI-powered applications using Generative AI and machine learning models.

• Integrate Large Language Models (LLMs) such as OpenAI, Azure OpenAI, or other foundation models into enterprise applications.

• Build AI copilots, chatbots, document intelligence solutions, and recommendation systems.

RAG & AI Architecture

• Implement Retrieval Augmented Generation (RAG) architectures using vector databases.

• Develop pipelines for embedding generation, semantic search, and contextual retrieval.

• Optimize prompts, context handling, and response accuracy.

Backend Engineering (.NET)

• Build scalable microservices and REST APIs using .NET Core / .NET 8+.

• Integrate AI models into backend systems and enterprise platforms.

• Implement asynchronous and event-driven processing for AI workloads.

Data & AI Pipelines

• Build pipelines for data ingestion, preprocessing, and model inference.

• Work with structured and unstructured data including documents, text, and knowledge bases.

AI Deployment & Operations

• Deploy AI models and services using containerization and cloud platforms.

• Implement monitoring, evaluation, and feedback loops for AI systems.

• Ensure security, scalability, and performance of AI services.

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Required Skills

Programming & Development

• Strong experience in C# / .NET Core / .NET 8

• Experience building scalable APIs and microservices

• Familiarity with Python for AI development

Generative AI

• Experience working with LLMs (OpenAI, Azure OpenAI, Claude, etc.)

• Knowledge of Prompt Engineering

• Experience implementing RAG architectures

• Experience with embeddings and semantic search

AI Frameworks & Tools

• Familiarity with LangChain, Semantic Kernel, or similar AI orchestration frameworks

• Experience with vector databases (Pinecone, Weaviate, Azure AI Search, etc.)

• Knowledge of ML frameworks such as PyTorch, TensorFlow, or Scikit-learn

Cloud & DevOps

• Experience with Azure / AWS / GCP

• Experience with Docker and CI/CD pipelines

• Understanding of MLOps practices