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AiDash Internship 2025 | Software Engineering Intern

๐ŸŒŸ 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!



๐Ÿ“ Job Details

๐Ÿ”น Job Role: Software Engineering Intern
๐ŸŽ“ Qualification: Fresh graduates or students currently pursuing engineering
๐Ÿ“… Batch: 2024, 2025, or recent graduates
๐Ÿ†• Experience: Freshers
๐Ÿ’ฐ Stipend: ₹50,000 – ₹60,000/month (as per Glassdoor)
๐Ÿ“ Location: Bengaluru & Gurugram
Last Date to Apply: ASAP

✅ Eligibility Criteria

๐ŸŽ“ Currently pursuing or recently completed a degree in Engineering, Computer Science, or related field
๐Ÿ’ป Programming basics with Python and/or Java
๐Ÿ—ƒ️ Familiarity with SQL or NoSQL databases
๐Ÿ—ฃ️ Excellent written and verbal communication skills
⚡ Self-motivated with strong collaboration skills
๐Ÿง  Interest in data engineering, analytics, or machine learning

๐Ÿงพ Job Description

๐Ÿ‘จ‍๐Ÿ’ป As a Software Engineering Intern at AiDash, you will:
๐Ÿ”ง Design and deliver data-driven solutions in collaboration with technical teams
๐Ÿ“Š Run, monitor, and maintain data/model pipelines for high-quality output
๐Ÿ“ฆ Ensure reliable, clean data for analytics and customer success teams
๐Ÿš€ Contribute to customer-focused solutions powered by AI-driven insights
๐Ÿง  Bring in new ideas, improve existing workflows, and enhance system performance
๐Ÿ“ˆ Learn and apply best practices in data engineering and analytics

๐ŸŒŸ Why Join AiDash?

๐Ÿš€ Impactful Work: See your code affect real-world infrastructure
๐Ÿ‘จ‍๐Ÿซ Mentorship: Work closely with engineering leaders
๐Ÿค Team Culture: Collaborate with GIS & Customer Success teams
๐Ÿ“š Learning Opportunity: Get hands-on with real-time data pipelines
๐ŸŒ Innovative Vision: Contribute to AI-based sustainability tech

๐Ÿข 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

๐ŸŽ–️ Certificate of Internship
๐Ÿ’ผ Potential PPO (Pre-Placement Offer)
๐Ÿ’ก Access to internal tech talks and skill workshops
๐ŸŒŸ Recognition for high-performing interns
๐Ÿงฉ Work with real-world client projects and live data

๐Ÿ“ฉ How to Apply?

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

๐Ÿ‘‰ For More Details & Apply: Click Here

❓ Top 5 Interview Questions & Sample Answers

๐Ÿ”น 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.

๐Ÿ”น 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.

๐Ÿ”น 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.

๐Ÿ”น 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.

๐Ÿ”น 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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