The reported HCLTech AI Engineer process had an HR profile discussion followed by a technical conversation on projects, AI models, RAG, GenAI, and model-selection reasoning.
$128K
Avg. Base Comp
$157K
Avg. Total Comp
2 rounds
Typical Rounds
Not reported
Process Length
For an HCLTech AI Engineer interview, one candidate reported a virtual process beginning with an HR discussion of their profile, experience and the role, then moving to a technical round. The technical conversation centered on the candidate’s projects and their understanding of AI models, RAG and generative AI.
The clearest preparation priority is explaining model selection with a clear rationale, not merely defining a model. Be ready to describe what a project required, why the chosen model fit those requirements, and how you would compare it with another plausible choice. The candidate was specifically asked why they chose a model and how they knew which model to choose.
Prepare concise project walkthroughs that state the problem, your contribution, the approach, the result and the reasoning behind major choices. HCLTech’s work in AI, GenAI and MLOps makes clear explanations of applied technical choices relevant role context. This guide reflects one reported candidate experience, so later rounds and timelines are not reported.
Synthesized from 1 candidate report by our editorial team.
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Real interview reports from people who went through the HCLTech process.
The interview process was pretty technical and hands-on. The first round was a machine coding challenge focused on building a RAG-based application, which tested both my implementation skills and understanding of retrieval-augmented generation concepts. I had to demonstrate how to work with vector databases and integrate them into a working solution under time pressure.
After that, I went through two technical rounds that dove deeper into the theory and deployment side of things. The interviewers asked detailed questions about RAG implementation strategies, how vector databases actually work under the hood, and the fundamentals of transformer architecture. It wasn't just about knowing the concepts — they wanted to see how I'd actually apply them in a production setting. The questions were intermediate level, so they expected solid fundamentals but also practical problem-solving ability.
Overall, the process felt fairly straightforward if you have the right background. The focus was clearly on hands-on AI and ML knowledge rather than abstract computer science theory. I got an offer, though I ended up declining it in the end.
Prep tip from this candidate
Focus heavily on RAG implementation details and vector database fundamentals — these are core to their technical assessment. Be prepared to build a working RAG application in a live coding round and explain transformer architecture at an intermediate level, not just conceptually but in terms of deployment considerations.
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Sourced from candidate reports and verified by our team.
Topics based on recent interview experiences.
Featured question at HCLTech
Which model do you pick given 85% and 82% accuracy
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Synthesized from candidate reports. Individual experiences may vary.
The candidate reports that HCLTech’s HR team made the initial contact for a virtual interview. No scheduling timeline or screening questions were reported.
The first reported round was an HR discussion about the candidate’s profile, prior experience and the role. Candidates may prepare a concise introduction and a clear account of relevant project experience.
The second reported round discussed projects, AI models, RAG and generative AI. Questions included what generative AI is, why a model was chosen, and how to decide which model to use; candidates should be ready to explain their reasoning clearly.
