Offcampus Internship (Direct test opportunity)

Quantiphi’s interview process typically starts with an online aptitude and coding test, followed by two technical interview rounds focusing on data structures, algorithms, and system design. The final HR dialogue assesses cultural fit and communication skills. Candidates should practice coding on platforms like LeetCode and be ready to discuss projects and problem‑solving approaches.

Quantiphi promotes a collaborative, learning‑centric environment where engineers are encouraged to experiment and innovate. The company offers flexible work hours, regular tech talks, and mentorship programs, fostering both professional growth and a healthy work‑life balance.

Eligible courses: B.Tech, M.Tech, MCA, MSc (any specialization). Batch: 2026 pass‑out. Academic merit: Minimum 70% in 10th, 12th, and cumulative graduation (CGPA to be converted as per Unstop guidelines). No active backlogs or history of re‑appears. Open to engineering, arts, commerce, science, and other streams meeting the above criteria.

Round 1: Online Test (Aptitude, Verbal & Coding) → Round 2: Interview Round‑1 → Round 3: Interview Round‑2 → Round 4: HR Dialogue

Quantiphi, founded in 2013, is an award‑winning AI‑first digital engineering firm that helps enterprises transform through data‑driven solutions. With a strong focus on machine learning, data analytics, and conversational AI, the company has built a reputation for delivering end‑to‑end digital transformation across sectors such as finance, healthcare, retail, and technology. Its culture blends cutting‑edge research with rapid product development, enabling engineers to work on real‑world problems that impact millions of users. Quantiphi’s commitment to innovation is reflected in its multiple patents, industry recognitions, and a growing global client base. The Off‑Campus Internship for the 2026 batch offers a six‑month, full‑time, in‑office experience across its Bangalore and Mumbai offices. Interns will be placed in one of four high‑impact tracks – Framework Engineer, Software Development, Platform Engineer, or Data Engineer – and will work alongside senior engineers on live projects that drive the company’s AI solutions. The role is designed to give fresh graduates hands‑on exposure to the entire product lifecycle, from requirement gathering and design to implementation, testing, and deployment. **Key Responsibilities** 1. Collaborate with product and engineering teams to understand project requirements and translate them into technical specifications. 2. Design, develop, and maintain scalable codebases using languages such as Python, Java, or C++. 3. Build and optimize data pipelines, ETL processes, and storage solutions for large‑scale datasets. 4. Implement machine‑learning models and integrate them into production services. 5. Conduct unit, integration, and performance testing to ensure code quality. 6. Participate in code reviews, providing and receiving constructive feedback. 7. Document technical designs, APIs, and deployment procedures. 8. Troubleshoot and resolve production issues under guidance from senior engineers. 9. Stay updated with emerging technologies and suggest improvements to existing systems. 10. Contribute to team knowledge‑sharing sessions and hackathons. **Tech Stack**: Python, Java, C++, SQL, NoSQL (MongoDB), Hadoop, Spark, AWS/GCP, Docker, Kubernetes, Git, Linux, RESTful APIs, and common ML libraries (TensorFlow, PyTorch). **Growth Path**: Successful interns may receive a full‑time offer (FTE) after the internship, with a clear progression from Junior Engineer to Senior Engineer, and eventually to Lead or Architect roles, depending on performance and domain expertise. **Why Join Quantiphi?** The internship provides a rare blend of startup agility and enterprise‑scale impact. Interns gain mentorship from industry experts, work on cutting‑edge AI projects, and receive a competitive stipend. The exposure to real clients, coupled with Quantiphi’s strong emphasis on learning and innovation, makes it an ideal launchpad for a career in data‑driven technology.