Good company - IOS Developer bei CodeCraft Technologies: Mitarbeiterbewertung

5,0
5. Juli 2019
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CEO-Befürwortung
Geschäftsprognose

Pros

1. Can get a lot of knowledge. Standard code level. 2. The best company for design(UI/UX), mobile development and web development 3. Opportunity to work on multiple platforms. 4. Good to learn as freshers. 5. Senior people are so helpful and quality professionals. 6. Yearly once outing(full fun night outs). 7. CEO and CTO both cool persons and very hard working. 8. Have an office in blore also. 9. No spoon-feeding, need to learn yourself. 10. Out-site for the project also started.

Kontras

1. No much employee benefits. 2. Some time CTO will be very rude, doesn't treat like human beings. He will use slangs too. But, he is the only guy who understands the problems of developers. ( technically ) 3. The HR team is like "If God gave a boon, the priest would not" type of management. If employee clocking hours is just less than 30min, HR would mark a day as LOP. So not flexibility. 4. Recently Increment was superb. before it was ok. 5. They will fire you without any prior notification. If you decide to move out, the notice period is 3 months as per the policy and it’s not negotiable. But if they want you to go out, they will fire you then and there and ask to move out the very same week. 6. Overall good company to work.

Mehr Bewertungen zu CodeCraft Technologies entdecken

3,0
9. Mai 2026
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CEO-Befürwortung
Geschäftsprognose

Pros

* Excellent for freshers who are entering to Software Engineering

Kontras

* No work life balance * HR policies keep changing / currently WFH has been removed and holidays reduced * There is lack of diversity and inclusion

5,0
9. Dez. 2025
Praktikant (anonym)
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CEO-Befürwortung
Geschäftsprognose

Pros

1. Good place to build strong fundamentals I got solid hands-on experience with Python, SQL, basic ML, and data cleaning, which strengthened my foundation. 2. Supportive environment for beginners The team was friendly and approachable, which made it easy to ask questions and learn at my own pace. 3. Exposure to end-to-end ML workflow I worked on the full cycle—data preprocessing, model building, evaluation, and reporting. 4. Real-time projects instead of dummy datasets I got to work with actual data, which helped me understand real industry challenges like missing values and data imbalance. 5. Opportunity to automate tasks I got experience in automating reporting and analytics tasks, which improved my practical Python skills.

Kontras

1. Limited exposure to advanced ML/AI tools The projects were mostly focused on basic ML models and analytics, so exposure to large-scale ML systems or GenAI frameworks was limited. 2. Smaller team → fewer mentorship opportunities The team size was small, so sometimes I had to figure things out myself without much technical guidance. 3. Project scope was narrow Many tasks were routine data cleaning, dashboards, and simple ML, so the opportunity to explore more complex pipelines or AI workflows was limited. 4. Tech stack was traditional Most work was Python + SQL + basic ML. Tools like RAG, LLM APIs, LangChain, and vector DBs weren’t used there.

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