AI Backend Developer( C# and .NET Core with AI integration )

Experience - 5-6 years

Location - Bengaluru/Hyderabad

Notice Period :Immediate joiner/15 days

• Key Responsibilities Design and implement backend services in C# and .NET Core with AI integration as a primary focus.
• Build and maintain RAG pipelines that connect LLMs with external knowledge bases, vector databases, and APIs.
• Architect and deploy agentic AI systems capable of autonomous task execution, orchestration, and decisionmaking.
• Integrate Generative AI models into backend workflows for summarization, content generation, and intelligent automation.
• Implement secure authentication and authorization using JWT tokens .
• Optimize MongoDB schemas and queries for AIdriven workloads.
• Apply design patterns to ensure maintainable, scalable, and reusable code.
• Troubleshoot AI pipelines and backend services , including performance bottlenecks, API failures, and orchestration issues.
• Collaborate with data scientists, ML engineers, and product teams to deliver AIpowered features.
• Write unit tests and integration tests to ensure reliability of AIenabled services.
• Required Skills & Qualifications Strong proficiency in C# and .NET Core .
• Experience building and consuming RESTful APIs .
• Handson knowledge of JWT tokenbased authentication .
• Solid understanding of MongoDB (schema design, indexing, aggregation).
• Strong troubleshooting and debugging skills for backend and AI pipelines.
• Experience with Generative AI models (LLMs) such as GPT, LLaMA, or similar.
• Understanding of RAG architectures and ability to integrate vector databases (e.g., Pinecone, Weaviate, Redis Vector).
• Exposure to agentic AI frameworks (LangChain, Semantic Kernel, AutoGen, etc.).
• Familiarity with common design patterns (Repository, Factory, Dependency Injection, etc.).
• Experience with version control systems (Git).

• NicetoHave Skills Experience with cloud AI platforms (Azure AI, AWS Bedrock, GCP Vertex AI).
• Knowledge of microservices architecture .
• Familiarity with CI/CD pipelines .
• Exposure to containerization tools (Docker, Kubernetes).
• Experience with AI orchestration tools and prompt engineering best practices .
• Knowledge of monitoring and observability tools for AI pipelines.