Ola placement papers, interview questions, placement process, eligibility criteria, and complete preparation guide for 2025-2026 campus recruitment and hiring.
Ola (ANI Technologies Pvt. Ltd.) is India’s leading mobility platform and one of the world’s largest ride-hailing companies. Founded in 2010 by Bhavish Aggarwal and Ankit Bhati, Ola is known for its technology-driven transportation, electric mobility, and digital innovation, serving millions of users across India and international markets.
Headquarters: Bengaluru, India
Employees: 7,000+ globally
Industry: Mobility, Technology
Revenue: $1.1+ Billion USD (2023)
Academic Requirements
Minimum Percentage: 65% or 6.5+ CGPA in 10th, 12th, and graduation
Degree: B.E./B.Tech/M.E./M.Tech/MCA in Computer Science, IT, ECE, EE, or related fields
Year of Study: Final year students and recent graduates (within 1 year)
Backlogs: No active backlogs at the time of selection
Branch Eligibility
Eligible Branches: CS, IT, ECE, EE, and related engineering streams
Programming Focus: Strong skills in Data Structures, Algorithms, and System Design
Experience: Freshers and up to 2 years experience (for entry-level SDE roles)
Additional Criteria
Coding Skills: Proficiency in at least one language (Java, C++, Python, Go)
Gap Years: Maximum 1 year gap allowed
Course Type: Full-time degrees only
Nationality: Indian citizens (for India roles)
Ola Placement Papers 2024
Ola Placement Papers 2025
Campus Recruitment
College Visits - Through placement cells at top engineering colleges
Direct registration via college coordinators
Off-Campus Drives
Ola Careers Portal - Apply online for Software Engineer and other roles
Participate in Ola Tech Hunt, hackathons, and coding contests
Online Assessment
Direct Apply - olacabs.com/careers
Register with academic details and resume
Online Coding Assessment (OA) - 90-120 minutes
Format: Conducted on platforms like HackerRank or Ola’s internal tool
Success Rate: ~10-15% advance to interviews
Technical Interviews (2-3 rounds, 45-60 min each)
Format: Virtual or onsite
Managerial/Team Fit Interview (1 round, 45 min)
Format: Senior engineer or manager
HR/Offer Discussion (20-30 min)
Format: HR Manager
| Phase | Duration | Key Activities |
|---|---|---|
| Online Assessment | 1 day | Coding, debugging |
| Technical Interviews | 1-2 weeks | DSA, system design |
| Managerial/Team Fit | 2-3 days | Ola values, teamwork |
| HR Discussion | Same day | Offer, negotiation |
| Result Declaration | 2-3 days | Offer letter, background check |
Array Manipulation
def max_subarray_sum(arr): max_sum = float('-inf') current_sum = 0 for num in arr: current_sum += num if current_sum > max_sum: max_sum = current_sum if current_sum < 0: current_sum = 0 return max_sumGraph Traversal
This problem is a classic example of finding the shortest path in a graph using algorithms like Dijkstra’s or Breadth-First Search (BFS). The problem statement typically involves a graph represented by an adjacency matrix or list, and two nodes (source and destination). The goal is to find the minimum number of edges or the minimum sum of weights from the source to the destination.
Example:
// Assuming a graph is represented using an adjacency matrix// int graph[V][V] = { {0, 4, 0, 0, 0, 0, 0, 8, 0},// {4, 0, 8, 0, 0, 0, 0, 11, 0},// {0, 8, 0, 7, 0, 4, 0, 0, 2},// {0, 0, 7, 0, 9, 14, 0, 0, 0},// {0, 0, 0, 9, 0, 10, 0, 0, 0},// {0, 0, 4, 14, 10, 0, 2, 0, 0},// {0, 0, 0, 0, 0, 2, 0, 1, 6},// {8, 11, 0, 0, 0, 0, 1, 0, 7},// {0, 0, 2, 0, 0, 0, 6, 7, 0} };// int src = 0;// int dest = 4;// int V = 9;// int dist[V];// bool sptSet[V];// for (int i = 0; i < V; i++)// dist[i] = INT_MAX, sptSet[i] = false;// dist[src] = 0;// for (int count = 0; count < V - 1; count++) {// int u = minDistance(dist, sptSet, V);// sptSet[u] = true;// for (int v = 0; v < V; v++)// if (!sptSet[v] && graph[u][v] && dist[u] != INT_MAX// && dist[u] + graph[u][v] < dist[v])// dist[v] = dist[u] + graph[u][v];// }// cout << "Shortest distance from " << src << " to " << dest << " is " << dist[dest];Dynamic Programming
This problem is a classic example of the Coin Change Problem, which is a variation of the Knapsack Problem. The goal is to find the minimum number of coins needed to make a given amount using a set of coin denominations. This problem can be solved using Dynamic Programming (DP) with a bottom-up approach.
Example:
int coinChange(int coins[], int m, int amount) { int dp[amount + 1]; dp[0] = 0; for (int i = 1; i <= amount; i++) dp[i] = INT_MAX; for (int i = 1; i <= amount; i++) { for (int j = 0; j < m; j++) { if (coins[j] <= i) { int sub_res = dp[i - coins[j]]; if (sub_res != INT_MAX && sub_res + 1 < dp[i]) dp[i] = sub_res + 1; } } } return dp[amount];}Design a Ride Matching System
Design a Payment Gateway
Customer Focus
Ownership
DSA Fundamentals
Arrays & Strings: Manipulation, searching, sorting
Trees & Graphs: Traversals, shortest path, DFS/BFS
Dynamic Programming: Memoization, tabulation
Hashing: Hash maps, sets
System Design Basics
Mobility Architecture: Ride matching, payments, driver allocation
Database Design: SQL vs NoSQL, normalization
API Design: RESTful APIs, authentication
Scalability: Load balancing, caching, sharding
“Tell me about yourself”
“Describe a time you worked in a team”
“Why Ola?”
“Are you willing to relocate?”
DSA Mastery
Priority: Critical
Time Allocation: 50%
System Design & OOP
Priority: High
Time Allocation: 20%
Ola Values
Priority: High
Time Allocation: 20%
Aptitude & Communication
Priority: Medium
Time Allocation: 10%
| Level | Experience | Base Salary | Total Package | Typical Background |
|---|---|---|---|---|
| SDE-1 | New Grad | ₹10-16 LPA | ₹16-22 LPA | Fresh graduates, top colleges |
| SDE-2 | 2-4 years | ₹18-24 LPA | ₹24-30 LPA | 2-4 years experience |
| Senior SDE | 5-8 years | ₹28-40 LPA | ₹40-55 LPA | Senior developers |
| Lead Engineer | 8+ years | ₹55+ LPA | ₹70 LPA+ | Architects, tech leads |
| Role | Level | Total Package | Requirements |
|---|---|---|---|
| QA Engineer | Entry-Mid | ₹7-12 LPA | Testing, automation |
| Product Manager | Mid-Senior | ₹18-35 LPA | Product sense, tech background |
| Data Scientist | Mid | ₹12-25 LPA | ML, analytics |
| DevOps Engineer | Entry-Mid | ₹8-18 LPA | Cloud, automation |
Hiring Trends 2025
Increased Virtual Hiring: More online assessments and interviews
System Design Emphasis: More focus in interviews
Diversity Hiring: Special drives for women and underrepresented groups
Process Changes
Online Assessments: More debugging and scenario-based questions
Team Fit Round: Mandatory for all SDE hires
Faster Offers: Reduced time from interview to offer
New Initiatives
Ola Tech Hunt: Coding competition for hiring
Student Programs: Internships, Ola Tech Internship
Internal Referrals: Employee referral program
Company Growth
Product Expansion: More engineering and data roles in India
Product Innovation: Mobility, electric vehicles, fintech
Global Mobility: Opportunities to work abroad
Ready to start your Ola preparation? Focus on DSA, system design, and Ola company values. Practice mock interviews and build strong STAR stories.
Pro Tip: Consistent practice on LeetCode and HackerRank is key. Understand Ola’s values and be ready to demonstrate them in behavioral rounds.