Senior Software Engineer-Full stack (C#, .NET Core)

Experience from 5 to 9 years

Desired Qualifications:

• 5+ years of Software Engineering experience or equivalent demonstrated through work experience, training, military experience, or education

• Strong hands-on development experience across modern technology stacks.

• Proven experience developing full-stack, scalable distributed applications

• Ability to work across multiple components and collaborate with cross-functional engineering teams

• Strong focus on delivering high-quality features aligned to product requirements and user stories

• Bachelor’s degree in engineering / MCA or equivalent

• Strong development experience in C#, .NET Core, REST/Web APIs, and Microsoft technologies

• Experience building scalable, distributed applications

• Experience with relational and non-relational databases, preferably in cloud environments

• Experience with asynchronous, event-driven, and messaging systems

• Exposure to cloud platforms (Azure/AWS/GCP) and containerization technologies

• Experience or understanding of Kubernetes preferred

• Solid understanding of operating systems (Windows/Linux) and virtualization

Job Expectations:

• Experience leveraging AI-assisted coding tools (e.g., GitHub Copilot, ChatGPT, or similar) to improve developer productivity, code quality, and delivery speed

• Working knowledge of AI/ML fundamentals (basic concepts, model lifecycle, limitations, evaluation)

• Experience integrating AI capabilities into applications using APIs/SDKs (e.g., Azure OpenAI/OpenAI/AWS/GCP AI services)

• Practical understanding of LLM usage patterns: prompt design basics, output validation, and guardrails

• Familiarity with common AI application architectures (e.g., Retrieval-Augmented Generation/RAG conceptually) and when to use them

• Ability to build AI-enabled features such as summarization, classification, extraction, Q&A, and conversational assistants

• Awareness of responsible AI practices: privacy, security, bias considerations, safe handling of data, and compliance basics

• Understanding of operational considerations for AI in production (latency/cost considerations, monitoring, fallback strategies, human-in-the-loop)

• Exposure to vector search / embeddings concepts and related storage options is a plus (not mandatory)