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REQUIREMENT_ID_1351 • 3-DAY_ACTIVE_POLICY JD

Analyst – Generative AI

Company
Company HCLTech
Type
Opportunity Type Internship
Salary
Stipend / Salary ₹4 LPA
Location
Location Bengaluru, India
Posted Date
Posted Date Yesterday
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Python LangChain LangGraph AI/ML Large Language Models Deep Learning Natural Language Processing Data Preprocessing Cloud AI Platforms Git Docker
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Aptitude practice questions and answers Open Resource ↗
Helps you prepare for the online assessment round by covering quantitative, logical and verbal topics commonly asked at HCLTech.
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Technical interview preparation guide Open Resource ↗
Provides coding patterns, AI/ML fundamentals and interview experiences useful for the technical interview at HCLTech.
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Comprehensive interview preparation portal Open Resource ↗
Offers curated interview questions, resume tips and mock interview resources tailored for freshers applying to HCLTech.
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Coding practice problem set Open Resource ↗
Contains a wide range of algorithmic problems to sharpen your Python coding skills for the technical round.

Engineering degree (any branch) with minimum 60% aggregate; 0–3 years of experience (fresh graduates eligible); must have strong Python programming skills; must have completed at least one AI/ML or LLM‑based project (academic, internship or self‑initiated); no active backlogs at the time of application; eligible batch years typically 2022‑2026.

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Round 1: Online aptitude test (logical reasoning, quantitative, verbal)
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Round 2: Technical interview (Python coding, AI/ML concepts, project discussion)
3
Round 3: HR interview (behavioral fit, communication, career aspirations)
HCLTech is a global technology services and solutions provider with a presence in over 50 countries. The company helps enterprises across banking, manufacturing, life‑sciences, telecom, retail and many other sectors to modernise their IT landscape, adopt cloud, and leverage emerging technologies such as artificial intelligence, data analytics and Internet of Things. With a workforce of more than 200,000 professionals, HCLTech is known for its "Mode 1‑Mode 2" strategy that blends traditional IT services with next‑generation digital offerings, fostering a culture of continuous learning and innovation. The Analyst – Generative AI role is positioned within HCLTech’s Artificial Intelligence practice and is targeted at fresh engineering graduates or early‑career professionals (0‑3 years). The position is advertised under the title "Analyst (Support & Operations)" but the actual work revolves around building and supporting generative‑AI agents. Candidates will work on cloud‑based AI platforms, use Python to develop pipelines, and contribute to the design, implementation and optimisation of large language model (LLM) driven solutions. The role offers a blend of hands‑on coding, data engineering and collaborative problem‑solving, making it an ideal launchpad for anyone passionate about the future of AI. Key responsibilities include: 1. Designing and developing AI agents using LangChain, LangGraph and Python. 2. Integrating generative‑AI models on cloud platforms such as AWS, Azure or GCP. 3. Collecting, cleaning and preprocessing data sets required for model training and evaluation. 4. Assisting senior engineers in fine‑tuning LLMs for specific business use‑cases. 5. Conducting performance benchmarking and suggesting optimisation strategies. 6. Preparing technical documentation, workflow diagrams and knowledge‑base articles. 7. Collaborating with cross‑functional teams to translate business requirements into AI solutions. 8. Participating in code reviews, unit testing and continuous integration pipelines. 9. Staying updated with the latest research in generative AI, LLMs and agentic systems. 10. Supporting the rollout of AI‑driven prototypes to production environments. Tech stack: Python, LangChain, LangGraph, PyTorch/TensorFlow, HuggingFace Transformers, REST APIs, Docker, Kubernetes, cloud services (AWS SageMaker, Azure AI, GCP Vertex AI), Git, CI/CD tools. Growth path: Analysts typically progress to Senior Analyst, then to AI Engineer, AI Solution Architect and eventually to Lead/Manager roles within the AI practice. HCLTech invests heavily in internal up‑skilling through its "Digital Academy" and offers certifications in cloud and AI technologies, ensuring a clear trajectory for career advancement. Why join HCLTech? The company provides exposure to large‑scale enterprise projects, mentorship from seasoned AI professionals, and a supportive environment that encourages experimentation with cutting‑edge generative‑AI tools. Employees benefit from flexible work‑from‑home policies, competitive compensation, and a strong emphasis on work‑life balance, making it an attractive destination for ambitious freshers.

HCLTech — AI & Machine Learning Interview Guide

Previously asked questions, exam syllabus, coding benchmarks & round strategy.

APTITUDE & LOGIC 📖
Aptitude Practice Questions & Online Mock Tests

Curated logical, quantitative, and verbal reasoning problems to sharpen reasoning skills required for the initial screening test.

Open Resource ↗
COMPANY GUIDE 🎯
HCLTech Interview Preparation Corner

Comprehensive guide covering typical interview formats, common questions, and preparation tips for HCLTech and off-campus tech roles.

Open Resource ↗
MOCK TESTS & PAPERS 📝
Comprehensive Interview Prep Resources & Syllabus

Collection of previous year questions, company-specific test patterns, and interview experiences for technical and HR rounds.

Open Resource ↗
CODING PRACTICE 💻
Algorithm and Data Structure Problem Set

Practice problems to improve coding proficiency and algorithm problem-solving speed for technical rounds.

Open Resource ↗
Round 1: Online Assessment (OA)
Aptitude, Quantitative Logic & 2 Coding Problems
Focus on accuracy and speed. Practice arrays, strings, and standard arithmetic puzzles.
Round 2: Technical Interview 1
Core Tech Stack (Python, LangChain, LangGraph, AI/ML, Large Language Models, Deep Learning, Natural Language Processing, Data Preprocessing, Cloud AI Platforms, Git, Docker) & Live Code Tracing
Explain your thought process aloud. Analyze time & space complexities before coding.
Round 3: System Design & Problem Solving
Database Schemas, APIs & Architecture Basics
Clarify edge cases, diagram schemas cleanly, and discuss scalability trade-offs.
Round 4: HR & Cultural Fit Discussion
HCLTech Core Values, Learning Agility & Offer Terms
Demonstrate passion, strong communication, and readiness for full-time collaboration.
What is the bias-variance tradeoff and how do you prevent overfitting? Answer ▼
Model Answer: High bias leads to underfitting (oversimplified model), high variance leads to overfitting (captures noise). Mitigate using L1/L2 Regularization, Dropout, Cross-Validation, and data augmentation.
Explain the difference between Precision, Recall, and F1-Score. Answer ▼
Model Answer: Precision = TP / (TP + FP) (correctness of positive predictions). Recall = TP / (TP + FN) (coverage of actual positives). F1-Score is the harmonic mean of Precision and Recall.
How does Gradient Descent work and what is the role of Learning Rate? Answer ▼
Model Answer: It optimizes loss functions by iteratively moving weights in the direction of negative gradient. A large learning rate may overshoot the minimum; a small rate causes slow convergence.
What is the difference between Supervised, Unsupervised, and Self-Supervised learning? Answer ▼
Model Answer: Supervised uses labeled data (X -> y). Unsupervised finds hidden patterns in unlabeled data (clustering/PCA). Self-supervised generates labels from input data (e.g. masked language modeling in BERT/Transformers).
Why do you want to join HCLTech as a Analyst – Generative AI?
Preparation Tip: Highlight HCLTech's market reputation, recent tech innovations, and how your skills in Python directly solve their team's objectives.
Describe a challenging bug or academic project roadblock and how you resolved it.
Preparation Tip: Use the STAR method: Situation (project context), Task (what needed solving), Action (specific tools/logic applied), Result (quantifiable positive outcome).
How do you handle strict deadlines or sudden scope changes?
Preparation Tip: Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.

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