Is Mindfire Solutions hiring on Feedback AI?

Mindfire Solutions has 2 active open roles on Feedback AI today. Roles are listed for New Delhi, India and other hubs where posted. Browse open engineering listings on this company profile or sign up free to see AI match scores for each role.

How many open roles does Mindfire Solutions have?

Feedback AI tracks 2 active Mindfire Solutions job listings right now. Counts refresh from live postings — inactive listings are removed from the public board.

How does Feedback AI match candidates to Mindfire Solutions jobs?

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What is it like to interview at Mindfire Solutions?

This profile includes 173 Glassdoor-backed interview experiences when enrichment gates pass — process notes, difficulty, and sample questions where available.

Mindfire Solutions

Computer Hardware Development · 51 to 200 Employees · New Delhi, India

4.3(758)

Mindfire Solutions headquartered in New Delhi, India has 2 active tech roles on Feedback AI with AI match scores, optional skill assessments, and company insights when Glassdoor enrichment exists. Candidates browse open listings on this profile; signed-in users see ranked fit and skill gaps per role. Employers post jobs and review match-ranked applicants on the same platform.

Founded 1999Revenue Unknown / Non-ApplicableCEO approval 0%Business outlook 78%

Office locations

Bhubaneshwar, India

Open roles tracked

Posted last 7 days

Posted last 30 days

Jobs at Mindfire Solutions ranked for your profile

Open roles at this company sorted by AI match score — skills, experience, and company fit when you are signed in. Guests browse the same roles on the public job board.

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Culture signals

Work-life balance 4.1
Compensation 3.7
Culture 4.0

What employees say

+ Mindfire solutions is the good company It provides good work culture

− Sometimes you won’t have projects so have to take training and can assigned any project

Employee voices

Recent Glassdoor reviews · 758 total

Senior Software Engineer· NoidaCurrent employee5.0/5

Good work culture, but project availability can vary

Pros: Mindfire solutions is the good company It provides good work culture

Cons: Sometimes you won’t have projects so have to take training and can assigned any project

View on Glassdoor

Software Engineer· ChennaiCurrent employee4.0/5

Nice work culture with friendly colleagues

Pros: Work Culture is very nice here.

Cons: Nothing bad , all are very friendly.

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Senior Software Engineer· NoidaCurrent employee2.0/5

Good for career starters, but very low pay as per industry standard.

Pros: Good for starting your career, but never stay there for long.

Cons: Very low salary per market standard

View on Glassdoor

SSE- Development· NoidaFormer employee4.0/5

Great place to start, but lacks challenging projects

Pros: Great company to start your carrier

Cons: Shortage of good Projects. Few legacy projects are there

View on Glassdoor

Senior Software Engineer· NoidaCurrent employee5.0/5

Very good company to work with

Pros: Good work culture Work flexibilty Permanent Work from home Good clients Good perks Work on latest tech

Cons: No Cons as such I can think of

View on Glassdoor

Company review data sourced from Glassdoor.

Compensation benchmarks

Glassdoor salary estimates for top hiring roles · 877 salary reports on Glassdoor

Role	Location	Median	Range (LPA)	Reports
Software Engineering Intern	India	6.5 LPA	6.0 LPA – 7.0 LPA	1

Salary benchmarks are estimates from Glassdoor and may differ from individual job listings.

Interview experiences

India-scoped Glassdoor interview reports · 173 total · avg difficulty 3.2/5 · 220 questions indexed

Sr Test Automation Engineer· NoidaAveragedecline offer

Interview 1- oops concept, coding Received offer letter Received call from HR( Padmavati), 6 days back from joining date. They scheduled another technical round, interviewer ( Shrikant Diwedi) he was bullying me and very rude , and rejected me. DO NOT JOIN THIS COMPANY EVER, there is no guarantee for job security.

Sample questions

Oops concept , hash map, even number

Software Engineer - AI/ML· IndiaVery_difficultno offer

I applied online and was shortlisted for the initial technical discussion. The interview was conducted over Google Meet with senior members of the technical team and lasted for about 1 hour and 2 minutes. It was a single round but covered multiple areas including DSA (string compression, character frequency, list rotation) and an in-depth discussion on Generative AI topics such as RAG architecture, vector databases, semantic search, LangChain, agent tools, memory handling, and model evaluation. The format was fast-paced with around 40 technical questions, mostly rapid-fire and detail-oriented. I was also asked to write a few lines of code on Colab while sharing my screen. There were no behavioral or HR questions in this round. The communication was clear, and instructions were shared beforehand regarding setup and expectations. I received the outcome after the interview. The interview went significantly deeper than a typical 2–4 year role, with advanced questions on internal workings of RAG systems, vector indexing (HNSW/IVF), caching strategies, LangChain tool mechanics, and precision/recall evaluation. The pace and breadth made it challenging even for someone with hands-on GenAI experience.

Sample questions

DSA questions - compress string (aaabbc --> a3b2c1) - char frequency in string - perform left rotation on a list by d, without using additional list Gen AI questions - Talk about recent gen ai project - Detailed question about RAG architecture ( pre processing, chunking, embedding, vector db) - How exactly storage and retrieval happens in vector db - Type of vector db and when to use which one - in memory vs persistent vector db - semantic search in detail, formula of cosin similarly - How to handle caching, if user uploads same file in a chatbot, how to handle - HNSW, IVF, what is the use and their working - HNSW available in which db - how you pass chat history to llm - how is chat history passed to llm in langchain - short term and long term memory, how to implement in langchain - how to build custom tools, in langchain / dspy/ crewai - tool decorator - how tool decorator uses docstring of tool function - Under the hood how tool decorator works - how to pass info like session ID to tool, and also not expose it to llm - how to pass tools to an agent - if a value is not passed by llm how to get that in the tool, will it fail? - LangGraph basics - Pydantic and its uses - what llms have you worked with (list all models / versions, eg: gpt 4 --> gpt 4.1 , 4o, 4 mini etc, gemini--> 1.5 flash 1.5 pro etc) - open source models - hugging face vs ollama - How to handle a CSV file in RAG - how to handle the table data (unstructured.io) - have you tried mistral for extracting table? - pypdf, pdfplumber and what other libraries have you tried for table extraction, which works best? - how to make image embeddings, what model is used - multimodal llms (take image/ audio/video/ file as input, and gives multimodal output) - Talk about any other project of yours - Few questions related to project - Have you also worked on ML models apart from gen ai? - How do you evaluate RAG? - What is precision and recall in RAGAS, also in ML, formula and definition - Any questions for me..

Senior Test Solutions Engineer· NoidaAverageaccept offer

There were two rounds of interview. 1. Technical 2. HR. In technical interview, it was for 1 hour and questions ranging from experience to test automation framework. The result comes in 30 mins after the interview and goes through a HR call for salary negotiation.

Sample questions

Automation Framework, Functional Testing and finding X-Path

Software Engineer· IndiaAveragedecline offer

The interview process typically begins with resume screening, followed by an initial HR call, technical and behavioral interviews, and concludes with final evaluations, reference checks, and a potential job offer.

Sample questions

one DSA questions, concepts of .NET

Test Engineer· IndiaAverageaccept offer

The interview consists of easy and medium level of questions if basic is good then brush some programs and expect the interviewer to be in positive mood. Apart from that high chances of getting selected if refferal is given by an employee with goodwill in the office.

Sample questions

Palindrome, Manual testing scenarios and concepts.

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