TCS Digital 2026: AI Topics in the Technical Interview

In FY26, 60% of TCS’s fresher hires are AI-skilled, per TCS CHRO Sudeep Kunnumal at the AI Impact Summit in March 2026. For students targeting the Digital track (₹7.0–7.5 LPA), that shift shows up directly in the technical interview.

The TCS hiring stack in 2026

TCS runs four hiring tracks for graduates, each with a separate NQT cutoff and technical depth:

Track	CTC	AI in selection
TCS Ninja	₹3.5–3.9 LPA	AI is a bonus, not a blocking criterion
TCS Digital	₹7.0–7.5 LPA	AI exposure helps; interviewers probe for it
TCS Prime	₹9.0–11.0 LPA	AI project review is part of selection; AI skills expected
TCS Smart Hiring	₹2.4–3.4 LPA	Separate funnel for BSc/BCA graduates; not the engineering NQT path

The Digital track sits at an inflection point. It is not yet as AI-intensive as Prime, but it is no longer as purely aptitude-plus-DSA as Ninja. In 2026, a strong NQT performance alone is unlikely to separate you at the Digital level. The technical interview is where AI exposure becomes the differentiator.

At the Ninja level, AI knowledge is a bonus. An exceptional aptitude-plus-DSA performance can carry you to a Ninja-track offer on coding skill and aptitude alone, with no ML background required. The Digital tier is a different calibration. The technical interview at Digital explicitly goes beyond data structures; it’s designed to surface whether a candidate understands AI-first development, not just whether they can solve a tree traversal problem.

Why the technical interview now surfaces AI topics

TCS is publicly operating as an “AI-first company.” Sudeep Kunnumal’s March 2026 interview puts the numbers on it: Prime and Digital cadre hiring grew 50% in volume over three years, while the AI-skilled proportion of the entire fresher cohort went from 10 to 15% to 60%; roughly 270,000 TCS employees are now in advanced AI skills, a 3x increase in a single year.

TCS also reduced its FY27 fresher intake to roughly 25,000 from 44,000 onboarded in FY26, with a heavier AI-skilled tilt. Fewer total seats, and the seats that remain skew further toward the Digital and Prime tiers. For students targeting Digital or Prime, the smaller intake makes a strong NQT performance more important to secure, not less.

The Digital-track technical interview reflects this direction. Interviewers are not looking for PhD-level ML depth at the Digital level. What they’re assessing is whether a candidate understands how AI systems function, can walk through a project they’ve actually built, and can reason about model behaviour rather than recite definitions.

What the AI section covers at the Digital level

The Digital interview is an advanced technical interview, not a subject exam. The AI component is part of a broader technical discussion that also covers data structures, problem-solving, and system design basics. Within the AI component, the topics that align with TCS’s stated AI-first direction fall into three areas:

Foundational ML concepts

• What a training-validation split is and why it matters
• Overfitting vs. underfitting: how to detect each and what to do about it
• Supervised vs. unsupervised learning: when you’d choose one over the other
• Evaluation metrics: accuracy, precision, recall, and why accuracy alone misleads on imbalanced datasets

These are breadth questions. The interviewer is checking whether you can use the vocabulary correctly and relate it to a practical context. Deriving backpropagation by hand is not the bar here.

Prompt reasoning and LLM basics

TCS’s shift to an AI-first delivery model means tools built on large language models (LLMs) are part of how delivery teams now work. Candidates who can articulate how prompt structure affects model output, what temperature controls, and why context-window limits matter will stand out at the Digital level.

That fluency looks like this in practice:

• Knowing that a prompt with a worked example (few-shot) generally outperforms a bare instruction (zero-shot) on structured output tasks
• Knowing that a temperature setting near 0 produces deterministic, consistent responses while higher values increase variation
• Understanding what hallucination means in LLM contexts and why output validation matters even with a well-crafted prompt

These are the kinds of reasoning an AI-first interviewer will probe for. You don’t need to have built a production system. You need to have spent enough hands-on time with these tools to reason about them under pressure.

Project walkthrough

If you have an AI or ML project on your resume or GitHub, the Digital-level interviewer will ask you to walk through it. They want to know:

• What problem you were solving and why it mattered
• What data you used and how you prepared it
• Which model or tool you chose and why
• What the result was and how you measured it

A small, well-understood project you can explain clearly is more valuable here than a large project you cannot. One GitHub-hosted project with a working demo beats a stack of passive course completions.

Two practical notes on making a project walk-through ready. First, document your choices in the README. An interviewer who can understand what the project does and why you built it that way, from a quick README scan, is already primed to ask useful questions rather than baseline verification ones. Second, make sure the project runs. A broken demo in a technical interview is recoverable; a project you haven’t touched in months and can no longer explain is not.

How to prep for the AI section in your placement window

A realistic 8 to 12-week plan for the Digital track, assuming a final-year placement window:

• Weeks 1 to 2: Close the core DSA gaps. TCS Digital’s technical interview still starts with coding. FACE Prep’s TCS coding questions and solutions covers the current question patterns.
• Weeks 3 to 4: Cover the NQT-level aptitude and verbal sections. FACE Prep’s TCS NQT preparation resource covers the current pattern and question types.
• Weeks 5 to 6: Study ML concepts at breadth. Overfitting, evaluation metrics, classification versus regression, and basic Python with scikit-learn. No specialised course required; targeted reading on each concept works.
• Weeks 7 to 8: Build one small project using an LLM API or a standard ML model on a real dataset. This is what you walk through in the Digital interview. It does not have to be complex. It has to be yours and it has to run.
• Weeks 9 to 12: Mock interviews, resume alignment, and NQT mock tests. The TCS Ninja questions and pattern resource is useful for aptitude-side benchmarking at NQT level.

For the wider context on how AI skills map to the full 2026 placement market, the 2026 AI roadmap for Indian engineering students covers the stack from Python basics through deployed projects.

The project you build in weeks 7 and 8 is what TinkerLLM is built for. At ₹299, it puts real LLM API calls in your hands with structured exercises that result in a micro-project you can put on your resume and GitHub. When a TCS Digital interviewer asks you to walk through something you’ve built with AI, that’s the answer: a working project you can open on your screen, not a certificate, not a course completion screenshot.