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AiDash Internship 2025 | Software Engineering Intern

🌟 AiDash, a cutting-edge AI-first SaaS company, is offering an exciting Internship for Software Engineering roles in Bengaluru and Gurugram. This opportunity is ideal for students or recent engineering graduates who are passionate about data pipelines, analytics, and software development. As a key part of AiDash’s Data Solutions team, you will work on transforming raw data into powerful, actionable insights that support global utility clients. If you're looking to kickstart your career in data engineering, Python, and ML applications, this is your golden ticket!



πŸ“ Job Details

πŸ”Ή Job Role: Software Engineering Intern
πŸŽ“ Qualification: Fresh graduates or students currently pursuing engineering
πŸ“… Batch: 2024, 2025, or recent graduates
πŸ†• Experience: Freshers
πŸ’° Stipend: ₹50,000 – ₹60,000/month (as per Glassdoor)
πŸ“ Location: Bengaluru & Gurugram
Last Date to Apply: ASAP

✅ Eligibility Criteria

πŸŽ“ Currently pursuing or recently completed a degree in Engineering, Computer Science, or related field
πŸ’» Programming basics with Python and/or Java
πŸ—ƒ️ Familiarity with SQL or NoSQL databases
πŸ—£️ Excellent written and verbal communication skills
⚡ Self-motivated with strong collaboration skills
🧠 Interest in data engineering, analytics, or machine learning

🧾 Job Description

πŸ‘¨‍πŸ’» As a Software Engineering Intern at AiDash, you will:
πŸ”§ Design and deliver data-driven solutions in collaboration with technical teams
πŸ“Š Run, monitor, and maintain data/model pipelines for high-quality output
πŸ“¦ Ensure reliable, clean data for analytics and customer success teams
πŸš€ Contribute to customer-focused solutions powered by AI-driven insights
🧠 Bring in new ideas, improve existing workflows, and enhance system performance
πŸ“ˆ Learn and apply best practices in data engineering and analytics

🌟 Why Join AiDash?

πŸš€ Impactful Work: See your code affect real-world infrastructure
πŸ‘¨‍🏫 Mentorship: Work closely with engineering leaders
🀝 Team Culture: Collaborate with GIS & Customer Success teams
πŸ“š Learning Opportunity: Get hands-on with real-time data pipelines
🌍 Innovative Vision: Contribute to AI-based sustainability tech

🏒 About AiDash 

AiDash is a leading SaaS company transforming critical infrastructure operations using satellite data and AI. With offices in the US and India, AiDash empowers industries to make proactive decisions using geospatial intelligence. Their platform blends machine learning, cloud-based analytics, and remote sensing to deliver actionable insights at scale. Be part of this revolution!

✨ Extra Perks Based on Internship Info

πŸŽ–️ Certificate of Internship
πŸ’Ό Potential PPO (Pre-Placement Offer)
πŸ’‘ Access to internal tech talks and skill workshops
🌟 Recognition for high-performing interns
🧩 Work with real-world client projects and live data

πŸ“© How to Apply?

All eligible and interested candidates can apply for AiDash Internship 2025 through the official link below:

πŸ‘‰ For More Details & Apply: Click Here

❓ Top 5 Interview Questions & Sample Answers

πŸ”Ή Q1. What is the difference between SQL and NoSQL databases?
A: SQL databases use structured tables and relationships, ideal for complex queries. NoSQL supports unstructured data, offering flexibility and scalability.

πŸ”Ή Q2. What steps would you take to ensure data quality in a pipeline?
A: Validate data formats, remove duplicates, check for nulls, apply consistent transformations, and use unit tests to automate checks.

πŸ”Ή Q3. How do you optimize Python code for large data sets?
A: Use generators, vectorized operations (NumPy/Pandas), avoid loops, profile the code for bottlenecks, and apply caching where applicable.

πŸ”Ή Q4. How would you handle performance bottlenecks in a model pipeline?
A: Monitor logs, identify slow stages, parallelize tasks, optimize algorithms or use batch processing for heavy loads.

πŸ”Ή Q5. What is the difference between supervised and unsupervised learning?
A: Supervised learning uses labeled data for training (e.g., classification), while unsupervised learning finds patterns in unlabeled data (e.g., clustering).

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