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AI/ML Full Stack Developer

Quest Global

  • Chennai, Tamil Nadu
  • Not disclosed
  • Fresher (0-1 Yrs)
  • Posted Today
HR
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About the role

Quest Global is a leading engineering services provider with a strong presence across multiple industries such as aerospace, automotive, energy, and information technology. Headquartered in Singapore, the company operates in over 30 countries and employs more than 12,000 engineers who deliver cutting‑edge solutions to Fortune‑500 clients. In India, Quest Global has established development centres in major tech hubs, fostering a culture of innovation, continuous learning, and global collaboration.

The organisation emphasizes employee growth through structured training programmes, mentorship, and exposure to real‑world projects that impact critical business outcomes.

The AI/ML Full Stack Developer role is designed for fresh engineering graduates who are passionate about building intelligent applications that blend robust front‑end experiences with powerful machine‑learning back‑ends. As a member of the Chennai development team, you will work closely with product managers, data scientists, and senior engineers to translate AI concepts into production‑ready software. This position offers a unique opportunity to gain hands‑on experience with generative AI, large language models, and modern cloud platforms while sharpening full‑stack development skills.

Key Responsibilities

  • Design and develop responsive web interfaces using React.js or Angular.
  • Implement server‑side APIs and services with Node.js or Python frameworks.
  • Integrate pre‑trained and custom machine‑learning models into web applications.
  • Build and maintain RESTful APIs for data exchange between front‑end and AI services.
  • Manage relational and NoSQL databases, ensuring data integrity and performance.
  • Deploy applications on cloud platforms (AWS, Azure, or Google Cloud) using Docker and CI/CD pipelines.
  • Conduct unit testing, debugging, and performance optimisation across the stack.
  • Collaborate with cross‑functional teams to define product requirements and technical specifications.
  • Stay updated with emerging AI/ML trends, generative AI tools, and vector‑database technologies.
  • Document code, APIs, and deployment processes for knowledge sharing.

Tech Stack: Python, JavaScript/TypeScript, React.js/Angular, Node.js, Flask/Django, REST APIs, PostgreSQL/MySQL, MongoDB, Docker, Git, AWS/Azure/GCP, LangChain/LlamaIndex, RAG, vector databases, CI/CD tools.

Growth Path: Starting as an associate developer, you can progress to senior full‑stack engineer, AI solutions architect, or product lead within 2‑4 years, supported by Quest Global’s internal training modules and global project exposure.

Why Join Quest Global? The company offers a vibrant, multicultural environment where fresh talent is mentored by industry veterans. Employees benefit from flexible work arrangements, competitive compensation, and a clear roadmap for technical and managerial advancement. By joining Quest Global, you become part of a global network that values innovation, quality, and personal development.

Eligibility & process

Who can apply

Bachelor's Degree in Computer Science, Information Technology, Electronics & Communication, or related engineering streams. Minimum aggregate of 60% (or CGPA 6.0/10). Final year students can apply if they can join full‑time after graduation. No active backlogs at the time of joining. Candidates from the 2023, 2024, 2025, or 2026 batches are eligible.

Skills & stack

Python JavaScript TypeScript React Angular Node.js Flask Django

Selection process

  1. Round 1: Online aptitude and coding test
  2. Round 2: Technical interview (coding, system design, AI/ML concepts)
  3. Round 3: HR interview (fitment, communication, expectations)

Prep kit

19 resources
Study material 4
Company-specific guides 4
Likely coding questions 4
Round-wise strategy 4
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, JavaScript, TypeScript, React, Angular, Node.js, Flask, Django) & 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 Quest Global Core Values, Learning Agility & Offer Terms Demonstrate passion, strong communication, and readiness for full-time collaboration.
Technical questions 4
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).
Behavioural & HR questions 3
Why do you want to join Quest Global as a AI/ML Full Stack Developer?
Highlight Quest Global'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.
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?
Explain your prioritization strategy, proactive communication with mentors/peers, and agile mindset.
Quest Global Not disclosed
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