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JD #2107
Applications open Internship

AI Engineer – Intern

Google

  • Ahmedabad
  • β‚Ή15,000 per month stipend; potential full‑time CTC β‚Ή3–4β€―LPA after conversion
  • Fresher (0-1 Yrs)
  • Posted Oct 24, 2026
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About the role

E2M Solutions is a fast‑growing technology services firm headquartered in Ahmedabad, Gujarat. The company specializes in delivering end‑to‑end AI and software solutions for clients across fintech, healthcare, and e‑commerce domains. With a strong focus on research‑driven product development, E2M has built a reputation for fostering innovation through collaborative teams and a culture that encourages continuous learning. Over the past few years, the firm has expanded its AI practice, integrating large language models (LLMs) and generative AI into real‑world applications, positioning itself as a go‑to partner for businesses looking to modernize their digital offerings. The AI Engineer – Intern role is designed for fresh graduates who are eager to translate theoretical AI knowledge into production‑grade solutions. Interns will work side‑by‑side with senior AI engineers and product managers, contributing to the design, development, and deployment of AI‑powered web applications. The position offers a six‑month paid internship with a clear pathway to a full‑time role, making it an ideal launchpad for a career in artificial intelligence and software engineering. Key Responsibilities: 1. Design and implement Python‑based AI modules that interact with LLM APIs such as OpenAI, Claude, or Gemini. 2. Develop and expose RESTful services using FastAPI or Flask to enable front‑end consumption. 3. Build responsive web interfaces with HTML, CSS, and JavaScript (or React/Next.js) to showcase AI features. 4. Perform CRUD operations on relational databases like PostgreSQL and cloud databases such as Supabase or Firebase. 5. Integrate vector databases and embeddings for Retrieval‑Augmented Generation (RAG) pipelines. 6. Write clean, version‑controlled code using Git and collaborate via GitHub pull‑requests. 7. Conduct unit testing, debugging, and performance profiling of AI workflows. 8. Document technical designs, API specifications, and deployment procedures. 9. Participate in code reviews and knowledge‑sharing sessions within the AI team. 10. Explore and prototype emerging AI agents and prompt‑engineering techniques. Tech Stack: Python, FastAPI/Flask, React/Next.js, LangChain/LangGraph, OpenAI/Claude/Gemini APIs, PostgreSQL, Supabase/Firebase, Git, Docker (optional). Growth Path: The internship serves as a talent pipeline for a full‑time AI Engineer position (CTC β‚Ή3–4β€―LPA). Successful interns receive mentorship, exposure to live client projects, and opportunities to lead small modules, accelerating their professional development. Why Join E2M Solutions? You will gain hands‑on experience with cutting‑edge generative AI tools, work in a collaborative office environment in Ahmedabad, receive a competitive stipend, and have a clear conversion route to a permanent role. The company’s emphasis on learning, mentorship, and real‑world impact makes it an excellent place to start a career in AI engineering.

Eligibility & process

Who can apply

Graduates (B.Tech/B.E., B.Sc., BCA, MCA, or equivalent) in Computer Science, Information Technology, Electronics, or related streams; Minimum 60% aggregate (or CGPA 6.0/10); Batch 2024‑2027; No active backlogs at the time of joining; Strong interest in AI/ML and software development.

Skills & stack

Python REST APIs HTML5 CSS3 JavaScript AI Machine Learning Large Language Models

Selection process

  1. Round 1: Online coding test
  2. Round 2: Technical interview (Python, API design, AI concepts)
  3. Round 3: HR interview (fit, motivation, culture)

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, REST APIs, HTML5, CSS3, JavaScript, AI, Machine Learning, Large Language Models) & 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 AI Engineer – Intern?
Highlight Google'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.
Google β‚Ή15,000 per month stipend; potential full‑time CTC β‚Ή3–4β€―LPA after conversion
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