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REQUIREMENT_ID_1414 β€’ 3-DAY_ACTIVE_POLICY

Technical Support Engineer – AIML

Company
Company GlobalLogic
Type
Opportunity Type Internship
Salary
Stipend / Salary 9 - 18 LPA
Location
Location Gurugram, India
Posted Date
Posted Date Sep 29, 2026
AI/ML Large Language Models Generative AI Cloud (GCP AWS Azure) Technical Support Troubleshooting API Integration Customer Communication Root Cause Analysis Documentation
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Aptitude Practice Questions Open Resource β†—
Helps candidates sharpen logical reasoning and quantitative skills required for the initial screening round at GlobalLogic.
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Company Interview Preparation Guide Open Resource β†—
Provides insights into typical interview patterns, sample questions and tips that are useful for GlobalLogic’s technical and HR rounds.
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Comprehensive Interview Prep Resources Open Resource β†—
Offers curated study material, mock interviews and experience sharing that can help candidates prepare for AI/ML and cloud‑focused discussions.
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Algorithm and Data Structure Problem Set Open Resource β†—
Enables candidates to practice coding problems that may be part of the technical assessment for troubleshooting and API handling scenarios.

Bachelor’s degree in Computer Science, Information Technology, Engineering or a related discipline (or equivalent practical experience). Minimum 1 year of experience in technical support, troubleshooting or a related technical field. Strong understanding of AI/ML concepts, chatbots, LLMs and Generative AI. Hands‑on experience with at least one cloud platform (GCP, AWS or Azure). No active backlogs; candidates should have cleared all semesters/years at the time of application. No specific percentage requirement, but a solid academic record (generally 60%+ CGPA) is preferred.

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Round 1: Aptitude & Logical Reasoning Test
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Round 2: Technical Interview (AI/ML concepts, cloud fundamentals, troubleshooting scenarios)
3
Round 3: HR Interview (fitment, communication skills, shift availability)
GlobalLogic is a leading digital engineering services firm and a proud member of the Hitachi Group. With a presence in more than 30 countries, the company partners with enterprises across technology, communications, healthcare, retail and telecommunications to build next‑generation digital products, platforms and services. Its engineering culture blends deep domain expertise in software, cloud, AI/ML and product design, enabling clients to accelerate innovation while maintaining high quality and security standards. Employees benefit from a collaborative environment, continuous learning programs, and exposure to cutting‑edge technologies that shape the future of digital transformation. The Technical Support Engineer – AIML role in Gurugram is a customer‑facing position that focuses on troubleshooting and supporting AI/ML‑driven solutions, including Generative AI applications, Large Language Models (LLMs), chatbots, and related APIs. The role sits at the intersection of AI product knowledge and cloud infrastructure, requiring engineers to diagnose complex issues, reproduce problems, and work closely with product and engineering teams to drive permanent fixes. Candidates will operate in a 24Γ—7 rotational shift model, providing timely assistance through email, chat, and phone channels while maintaining high customer satisfaction scores. Key Responsibilities: 1. Respond to customer queries across multiple channels and resolve technical issues related to AIML products. 2. Diagnose, reproduce, and troubleshoot problems in AI/ML models, Generative AI pipelines, and associated APIs. 3. Provide guidance on cloud deployments (GCP, AWS, Azure) and assist customers in configuring and optimizing their environments. 4. Conduct root‑cause analysis, document findings, and create detailed incident reports for engineering teams. 5. Collaborate with cross‑functional teams to escalate and track complex issues requiring product changes. 6. Develop and maintain knowledge‑base articles, troubleshooting guides, and standard operating procedures. 7. Ensure adherence to Service Level Objectives (SLOs) for response and resolution times. 8. Participate in on‑call rotations and hand‑over processes to guarantee 24Γ—7 coverage. 9. Offer proactive recommendations for performance tuning, security hardening, and cost optimization of AI workloads. 10. Stay updated with the latest advancements in AI/ML, LLMs, and cloud services to provide informed support. Tech Stack: AI/ML fundamentals, Large Language Models, Generative AI, RESTful APIs, JSON, HTTP, GCP/AWS/Azure services, Linux/Windows environments, monitoring tools, ticketing systems. Growth Path: Successful engineers can progress to Senior Technical Support Engineer, Technical Lead, or move into specialized AI/ML engineering, cloud architecture, or product management roles within GlobalLogic’s extensive portfolio. Why Join GlobalLogic? The company offers exposure to world‑class AI projects, mentorship from industry veterans, and a culture that rewards curiosity and continuous improvement. Employees enjoy flexible learning budgets, internal mobility, and the chance to work on products that impact millions of users globally.

GlobalLogic β€” AI & Machine Learning Interview Guide

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

APTITUDE & LOGIC πŸ“–
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 β†—
COMPANY GUIDE 🎯
GlobalLogic Interview Preparation Corner

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

Open Resource β†—
MOCK TESTS & PAPERS πŸ“
Comprehensive Interview Prep Resources & Syllabus

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

Open Resource β†—
CODING PRACTICE πŸ’»
Algorithm and Data Structure Problem Set

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

Open Resource β†—
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 (AI/ML, Large Language Models, Generative AI, Cloud (GCP, AWS, Azure), Technical Support, Troubleshooting, API Integration, Customer Communication, Root Cause Analysis, Documentation) & 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
GlobalLogic 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 β–Ό
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 β–Ό
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 β–Ό
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 β–Ό
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 GlobalLogic as a Technical Support Engineer – AIML?
Preparation Tip: Highlight GlobalLogic's market reputation, recent tech innovations, and how your skills in AI/ML 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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