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REQUIREMENT_ID_1962 • 3-DAY_ACTIVE_POLICY JD

AI Engineer – Intern

Company
Company Google
Type
Opportunity Type Internship
Salary
Stipend / Salary ₹15,000 per month stipend; potential full‑time CTC ₹3–4 LPA after conversion
Location
Location Ahmedabad
Posted Date
Posted Date Yesterday
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Python REST APIs HTML CSS JavaScript SQL LLM APIs AI tools Git problem solving
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Placement Papers for Leading Tech Companies Open Resource ↗
Curated set of placement papers that help candidates practice quantitative and logical reasoning required for tech interviews.
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Interview Experiences and Process Insights Open Resource ↗
First‑hand accounts of interview rounds and preparation tips that are valuable for aspiring AI engineers at Google.
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Comprehensive Interview Preparation Guide Open Resource ↗
A structured guide covering topics, sample questions, and strategies to ace technical interviews at top tech firms.
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Algorithm Practice Problems Open Resource ↗
Extensive problem set to sharpen coding skills and algorithmic thinking essential for coding assessments.

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.

1
Round 1: Online coding test
2
Round 2: Technical interview (Python, API design, AI concepts)
3
Round 3: HR interview (fit, motivation, culture)
E2M Solutions is partnering with Google’s Docs platform to offer a hands‑on internship that immerses fresh talent in the world of artificial intelligence and large language models. Google, a global technology leader, is renowned for its culture of innovation, relentless focus on user‑centric products, and a work environment that encourages curiosity and collaboration. The Docs suite, accessed via docs.google.com, serves millions of users daily, enabling real‑time collaboration, intelligent suggestions, and seamless integration with Google’s AI ecosystem. As part of this ecosystem, the AI Engineer Intern will contribute to building smarter, more intuitive document experiences. In this role, you will work closely with senior AI engineers and product managers to prototype, develop, and test AI‑driven features for Google Docs. You will gain exposure to end‑to‑end product development, from designing API contracts to integrating cutting‑edge LLM services such as OpenAI, Claude, or Gemini. The internship is structured to provide a clear growth path: successful interns may be offered a full‑time position with a competitive CTC of 3‑4 LPA, along with mentorship and continuous learning opportunities. Key Responsibilities: 1. Develop and test RESTful APIs that connect Docs front‑end components with AI back‑end services. 2. Write clean, modular Python code to prototype AI features such as smart suggestions, summarisation, and content generation. 3. Collaborate with front‑end developers to integrate AI outputs using HTML, CSS, and JavaScript. 4. Perform CRUD operations on relational databases to store user interaction data for model training. 5. Explore and integrate LLM APIs (OpenAI, Claude, Gemini) to enhance document intelligence. 6. Utilize AI productivity tools like ChatGPT, Claude, Cursor, and Replit to accelerate development cycles. 7. Conduct unit testing, debugging, and performance profiling of AI modules. 8. Document code, APIs, and design decisions for knowledge sharing within the team. 9. Participate in sprint planning, code reviews, and agile ceremonies. 10. Stay updated with the latest AI research and propose innovative ideas for product improvement. Tech Stack: Python, Flask/Django (API layer), HTML/CSS/JavaScript, SQL/NoSQL databases, REST APIs, LLM APIs (OpenAI, Claude, Gemini), Git, Docker, AI tooling (ChatGPT, Replit). Growth Path: Intern → Junior AI Engineer → AI Engineer → Senior AI Engineer → AI Product Lead. The program emphasizes mentorship, continuous skill upgrades, and exposure to large‑scale production systems. Why Join? You will work on a product used by billions, gain real‑world AI implementation experience, and be part of a culture that values learning, diversity, and work‑life balance. The stipend provides immediate financial support, while the conversion opportunity offers a stable career trajectory within one of the world’s most respected tech firms.

Google — Data Analytics & SQL 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.

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COMPANY GUIDE 🎯
Google Interview Preparation Corner

Comprehensive guide covering typical interview formats, common questions, and preparation tips for Google 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, REST APIs, HTML, CSS, JavaScript, SQL, LLM APIs, AI tools, Git, problem solving) & 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.
What is the difference between WHERE and HAVING clauses in SQL? Answer ▼
Model Answer: WHERE filters rows before any groupings are applied, while HAVING filters aggregated groups after GROUP BY has executed.
Explain SQL Window functions: ROW_NUMBER(), RANK(), and DENSE_RANK(). Answer ▼
Model Answer: ROW_NUMBER() assigns unique sequential integers. RANK() assigns identical ranks to ties and skips ranks. DENSE_RANK() assigns identical ranks to ties without skipping rank numbers.
How do you handle NULL and missing values during data cleaning in Python/Pandas? Answer ▼
Model Answer: Use .isna().sum() to identify missing values. Impute with mean/median using .fillna() or remove with .dropna(subset=[...]) depending on variance impact.
What is the difference between Star Schema and Snowflake Schema in Data Warehousing? Answer ▼
Model Answer: Star Schema has denormalized dimension tables directly connected to the central Fact table. Snowflake Schema normalizes dimension tables into sub-dimensions to minimize redundancy.
How do you calculate MoM (Month-over-Month) growth in SQL? Answer ▼
Model Answer: Use LAG(revenue, 1) OVER (ORDER BY month) to fetch the previous month's revenue and compute (revenue - prev_revenue) / prev_revenue * 100.
Why do you want to join Google as a AI Engineer – Intern?
Preparation Tip: 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.
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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