Interview Experience — UKG (Software Engineer)
Applied via: LinkedIn (no referral) — Virtual interview
Schedule: 4 elimination rounds across July 11–17
Rounds: 2 Technical, 1 Tech + Behavioral, 1 HR
Quick intro I gave
Short summary of education, internships (NXP, MyParticipants), and relevant projects (CropStop, AI Self-Driving Car). Emphasised hands-on ML/AI work and backend experience.
Round 1 — Technical (Principal SE) — 11 July
- Focus: deep dive into my internships/projects, AI/ML fundamentals, and a coding exercise.
- Project questions: data pipelines, model choices, evaluation metrics, deployment decisions, edge cases. Lots of cross-questions on implementation details.
- DSA (easy → medium):
- Implement merge sort and quick sort; discuss time/space complexity and stability.
- 3-Sum: Given an integer array nums, return all unique triplets [a, b, c] such that a + b + c == 0.
- ML/GenAI topics: model selection, regularisation, monitoring, and practical GenAI concerns (fine-tuning pitfalls, inference latency).
Round 2 — Technical (Senior Principal SE) — 15 July
- Focus: backend (databases & APIs) plus ML systems.
- DB topics: indexing, views vs materialized views, normalization vs denormalization, OLTP vs OLAP, query optimization, caching and sharding strategies.
- Example SQL task: joining three tables to fetch candidate names, interview dates, and average feedback score.
- ML systems: model serving, feature stores, Deep Q-Learning concepts (Q-function, experience replay, target networks), agentic vs generative AI, and tooling (LangChain vs LangGraph).
Round 3 — Technical + Behavioral (Director SE) — 16 July
- Focus: project ownership, product thinking, and behavioral fit.
- Demoed my n8n Interview Prep Assistant (built to qualify candidates against a job description and generate personalized technical + behavioral questions): explained purpose, workflow, user inputs, LLM usage, and how qualification → question generation works; showed deployed demo.
- Behavioral: motivation for AIML, handling ambiguous requirements, and dealing with failing models (rollback, investigation, communication).
Round 4 — HR (Senior Talent Acquisition) — 17 July
- Focus: fit, offers, relocation, and company/product knowledge.
- Tough HR questions on other offers, retention intentions, and reasons for choosing UKG.
- Location preference: chose Pune (from Bangalore) — reasoned about learning opportunities and closer access to senior engineers.
- Recommended: research UKG products & use cases before the HR call — it was helpful.
Closing note
A focused, technical loop where strong project knowledge, practical ML/system thinking, and a short deployed demo made a clear impact.