Tanisha Systems Inc interview process & preparation
Tanisha Systems Inc currently has 13 live openings on OnJob.io, concentrated in Product Manager, Project Manager and .NET Developer roles. There are no leaked or company-specific questions here — prepare with the general, role-based interview rounds and most-asked questions for those role types below.
OnJob.io does not publish leaked or company-confidential interview questions. The guides below are general, role-based preparation — the standard rounds and commonly asked questions for the role types Tanisha Systems Inc is currently hiring for. Your real Tanisha Systems Inc interview may differ.
What to expect when interviewing for Product Manager, Project Manager and .NET Developer roles at companies like Tanisha Systems Inc
Tanisha Systems Inc is currently advertising these role types on OnJob.io. Each guide is the general interview structure and most-asked questions for that role.
General Product Manager interview prep
2 live roles at Tanisha Systems Inc
Product sense, analytical thinking, prioritisation and metrics — the PM interview for product companies, startups and consumer apps.
Typical Product Manager interview rounds
01
Product sense / design
Design or improve a product for a target user.
02
Analytical / metrics
Define success metrics, diagnose a metric drop, size a market.
03
Execution & prioritisation
Roadmaps, trade-offs and stakeholder management.
04
Behavioural / leadership
Past products shipped, conflict resolution and influence.
Commonly asked Product Manager questions
How would you improve our product (e.g. WhatsApp, Swiggy, or our app)?
Design a product for [a specific user group, e.g. delivery riders or senior citizens].
What is your favourite product and why? How would you improve it?
Daily active users dropped 15% overnight — how do you investigate?
How would you decide what to build next with limited engineering resources?
What metrics would you track for a feature you just launched?
How would you estimate the market size for [a product] in India?
Walk me through how you'd prioritise a backlog of 20 feature requests.
C#, ASP.NET Core and SQL Server — the enterprise backend interview favoured by GCCs, banking, insurance and Microsoft-stack services firms across India.
Typical .NET Developer interview rounds
01
C# / OOP fundamentals
Language features, OOP principles, value vs reference types, collections and LINQ.
02
ASP.NET Core round
Web API design, middleware pipeline, dependency injection and Entity Framework.
03
Database & SQL Server
Schema design, stored procedures, indexing and query tuning.
04
System design / project deep-dive
Designing a scalable service and discussing your past .NET projects.
Commonly asked .NET Developer questions
What is the difference between value types and reference types in C#?
Explain the difference between an abstract class and an interface in C#.
What is the difference between IEnumerable, ICollection and IList?
What are async and await, and how do they work in C#?
Explain the difference between .NET Framework, .NET Core and .NET 5+.
What is dependency injection and how does ASP.NET Core support it out of the box?
Explain the request middleware pipeline in ASP.NET Core.
What is the difference between String and StringBuilder?
LLMs, prompt engineering, RAG and AI application development — the fast-growing generative-AI engineering interview at Indian product startups, GCCs and AI-first companies. Distinct from a classic ML engineer: the focus is building on top of foundation models, not training them.
Typical AI Engineer interview rounds
01
LLM & GenAI fundamentals
How transformers and LLMs work, tokens, embeddings, context windows and prompting.
02
RAG & application design
Retrieval-augmented generation, vector databases, chunking and grounding.
03
Coding round
Python coding plus building an API around an LLM with a framework like LangChain.
04
System design & evaluation
Designing a production AI feature, handling cost, latency, safety and evaluation.
Commonly asked AI Engineer questions
What is the difference between fine-tuning, prompt engineering and retrieval-augmented generation?
Explain how RAG works end to end, from a user query to a grounded answer.
What are embeddings and how are they used in semantic search?
What is a vector database and how does similarity search work?
What is a context window, and how do you handle inputs that exceed it?
What causes hallucinations in LLMs and how do you reduce them?
What is the difference between temperature and top-p sampling?
How would you chunk documents for a RAG pipeline, and why does chunk size matter?