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Full-Stack AI Intern

Google

  • Bengaluru, India
  • {'@type': 'QuantitativeValue', 'value': '', 'unitText': 'YEAR'}
  • Internship
  • Posted Oct 24, 2026
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About the role

GoComet is a fast‑growing technology company that is redefining global logistics through AI‑driven automation. Founded with the vision of making freight movement seamless, the firm combines deep domain expertise in supply chain with cutting‑edge machine learning, enabling shippers and carriers to make data‑backed decisions in real time. With a presence across major logistics hubs in India and a client base that spans continents, GoComet offers a vibrant environment where innovation meets real‑world impact. The organization invests heavily in research and development, fostering a culture where engineers are encouraged to experiment, iterate, and ship solutions that directly influence the efficiency of global trade. The Full‑Stack AI Intern role sits within the Nova – Autonomous Outcome Engineering team, a group dedicated to building intelligent agents that automate complex business processes. Interns will work side‑by‑side with senior engineers, product managers, and data scientists to design, develop, and deploy end‑to‑end AI solutions. This is a hands‑on opportunity to contribute to production‑grade applications that leverage Large Language Models (LLMs), Retrieval‑Augmented Generation (RAG) pipelines, and modern web frameworks. The role is ideal for candidates who are passionate about both front‑end user experiences and back‑end AI orchestration. Key Responsibilities: 1. Design and implement full‑stack web applications using React, TypeScript, and Node.js. 2. Develop Python services that interact with LLMs and RAG pipelines for document extraction and analytics. 3. Build and expose RESTful APIs to integrate AI agents with enterprise systems. 4. Create multi‑step AI workflows, orchestrating agents to automate logistics processes. 5. Write efficient ClickHouse queries and manage data pipelines for real‑time insights. 6. Implement event‑driven architectures using Kafka to ensure scalable communication. 7. Conduct unit, integration, and performance testing to guarantee reliability. 8. Deploy applications on cloud platforms, set up monitoring, and iterate based on feedback. 9. Collaborate with clients to gather requirements, demo prototypes, and refine solutions. 10. Document code, APIs, and workflow configurations for knowledge sharing. Tech Stack: React.js, TypeScript, Python, Node.js, REST APIs, LLMs (e.g., GPT‑4), LangGraph, RAG, ClickHouse, Kafka, YAML, Git, CI/CD pipelines. Growth Path: Successful interns can transition to full‑time Software Engineer or AI Engineer roles, with opportunities to lead product modules, specialize in AI research, or move into product management. The exposure to end‑to‑end AI systems accelerates learning and opens doors to senior technical positions. Why Join GoComet? The internship offers a rare blend of full‑stack development and cutting‑edge AI engineering, providing tangible impact on global logistics. Interns gain mentorship from industry veterans, access to state‑of‑the‑art tools, and the chance to see their code powering real customers. The fast‑paced, collaborative environment ensures rapid skill growth and a strong foundation for a career in AI‑enabled software development.

Eligibility & process

Who can apply

Eligible candidates must be pursuing a B.Tech, B.E, M.Tech, M.Sc or equivalent degree in Computer Science, Information Technology, Electronics & Communication, or related fields. Minimum CGPA/percentage of 6.5/70% (or equivalent) is required. Candidates from the graduating batches of 2023, 2024, 2025, or 2026 are welcome. No active backlogs are allowed at the time of joining. Strong programming fundamentals and a demonstrable interest in AI/ML or full‑stack development are essential.

Skills & stack

React Python Node.js JavaScript TypeScript REST APIs LLMs

Selection process

  1. Round 1: Online coding assessment
  2. Round 2: Technical interview (focus on full‑stack and AI concepts)
  3. Round 3: HR interview (fit and motivation)

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 (React, Python, Node.js, JavaScript, TypeScript, REST APIs, LLMs) & 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 Google 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 Google as a Full-Stack AI Intern?
Highlight Google's market reputation, recent tech innovations, and how your skills in React 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.
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