IBM | Cloud & AI Engineer | 6.5 YOE | Bengaluru | 2 Technical Rounds | Rejected

Hi everyone,

I recently interviewed with IBM for a Cloud & AI Engineer role in Bengaluru. I completed two technical rounds but unfortunately wasn't selected to proceed further.

Sharing my experience in case it helps others preparing for similar Backend, Cloud, or Platform Engineering roles.

My Background:

• YOE: 6.5 years
• Current Company: Oracle
• Role: Principal Data Systems Engineer
• Tech Stack: Java, Python, Kubernetes (OKE), OCI, REST APIs, Distributed Systems
• Interview Language: Java

The role was described as primarily Platform Engineering, involving Go and some DevOps work.

Round 1 - Technical Interview (Oct 28)

The interview was more focused on project discussions and fundamentals than DSA.

1. Introduction and Projects

• Explain your current work and recent projects.
• Walk through the high-level design of one of your projects.
• Explain the architecture and your contributions.

I discussed my work on fleet telemetry, observability, and event-driven systems.

2. Docker and Kubernetes

• Questions on Docker and Kubernetes fundamentals.
• Discussion about my hands-on experience with these technologies.

3. Coding

• LeetCode 206 - Reverse Linked List
• Debug an existing implementation of Merge Two Sorted Lists.

I completed both questions.

Result: Cleared.

The interviewer suggested preparing Terraform, Python, project discussions, and system design for Round 2.

Round 2 - Technical Interview (Oct 30)

This round was largely focused on project deep dives, cloud-native technologies, and practical engineering knowledge.

Questions Asked

Projects and Linux

• Explain your recent projects and architecture decisions.
• What is Linux inotify, and where have you used it?
• What happens if inotify generates duplicate notifications?

Cloud and Kubernetes

• What is the difference between Kubernetes and OKE?
• What is Helm?
• How do Helm charts help in Kubernetes deployments?

Networking and Security

• REST vs gRPC.
• TLS vs mTLS.
• How does certificate verification work?
• Why does a browser sometimes show a certificate warning with an Advanced/Proceed option?

AI and Vector Databases

• What is a vector database?
• Where have you used vector databases?
• How does semantic similarity work?
• How would a system distinguish between Saswat and saswat, and determine which is more relevant?

Coding

• LeetCode 5 - Longest Palindromic Substring

I was able to solve the coding question successfully.

Most of the discussion went reasonably well, although I wasn't fully satisfied with a few answers around Helm, gRPC, Kubernetes internals, and vector similarity.

Result: Not selected.

HR communicated the decision on November 2. Detailed interview feedback wasn't available, so I don't know the exact reason for rejection.

Key Takeaways

1. DSA alone isn't sufficient for experienced roles. I had prepared extensively for DSA, but both interviews focused significantly more on projects, cloud infrastructure, and technical fundamentals. The coding questions were relatively manageable.
2. Be prepared to defend every technology on your resume. If you've mentioned Kubernetes, Terraform, Helm, or LLM integration, expect questions about how these technologies work—not just where you've used them.
3. Understand your projects beyond the high-level design. Be prepared to discuss implementation details, failure handling, security, architecture trade-offs, and your individual contributions.
4. Backend and Platform Engineering expectations can differ. My experience is primarily Java/Python backend engineering and cloud infrastructure development. This role appeared to require deeper platform engineering knowledge.
5. Be transparent about your hands-on experience. It's better to acknowledge limited experience with a technology than bluff. However, you should still have a solid understanding of its fundamentals.

Final Thoughts

Although I wasn't selected, the interview process was a valuable learning experience.

My biggest takeaway is that for engineers with 5+ YOE, project depth, system design, and cloud-native fundamentals can be just as important as DSA.

I hope this helps others preparing for similar roles.