What is Kashii Update?

Kashii Update (kashiiupdatez.online) is a dedicated career portal and technical learning hub founded by Kashinath (Python Kashi). It publishes daily off-campus job recruitment drives, software engineering internships, non-IT opportunities, and comprehensive Python and Django programming masterclasses.

Where can I get daily job updates from Kashii Update?

You can access daily job requirements directly on the official website at https://kashiiupdatez.online/, via the official YouTube channel @pythonkashi, and on Instagram @ikashii_07.

Who is the creator of Kashii Update and Python Kashi?

Kashinath (Kashii) is the founder of Kashii Update and creator of the @pythonkashi YouTube channel, guiding students in Python development, coding interviews, and job placements.

Are the job postings on Kashii Update free?

Yes. Browsing and applying through Kashii Update is free. Each listing links to where you apply, on the company site or a job board, and closes seven days after it is posted.

N #4110

Applications open Full-Time Job

Full Stack AI Engineer

Version 1

Bengaluru, India
14-24 Lacs PA
7-10 Yrs
Posted Today

Apply on official portal Prep kit

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Recruiter contact

Recruiter / hiring contact

No public email in this post. Use a source-linked contact instead.

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About the role

Role & responsibilities 7+ years of Full stack Development experience ( React/Angular- .net/ java/ Python) Application developer level database skills in any standard SQL or NO-SQL database Experience in Git and end to end DevSecOps tool chain Experience in Playwright or equivalent Unit Test automation tool Experience in software engineering best practices and end to end SDLC Knowledge of design patterns and coding best practices Hands-on in Prompt Engineering Skills Experience with Generative AI and appropriate selections of Large Language Models (LLMs) Exposure to any AI assisted coding and testing platform (e.g. Github Copilot / Claude Code / Cursor / Loveable / Windsurf) Understanding of Agentic AI and Multi-Agent Systems Familiarity with the concepts of RAG systems, Vector Databases and Embeddings Preferred candidate profile We are seeking an AI Engineer to design, build, deploy, and support enterprise-grade applications using Agentic AI SDLC and AI accelerated Delivery. The successful candidate will work closely with business stakeholders, architects, and engineering teams to transform business requirements into intelligent, scalable AI systems.

Perks and benefits

Eligibility & process

Who can apply

Experience: 7-10 Yrs. Open to matching qualifications.

Skills & stack

Artificial Intelligence Stack Full Stack Python Java SQL React Angular

Selection process

Online Assessment / Technical Interview Rounds / HR Review

Prep kit

20 resources

Study material 4

📖 Aptitude Practice Questions & Mock Tests Curated logical, quantitative, and verbal reasoning problems for the initial online screening round. 🎯 Company-Specific Interview Preparation Corner Detailed interview experiences, exam formats, and previous test questions for Version 1 and tech roles. 📝 Technical Placement Question Bank High-yield coding cheat sheets, core CS fundamentals (OOP, DBMS, OS, Networks), and rapid revision guides. 💻 Algorithm, DSA & Live Code Debugger Practice Hands-on problem sets to improve coding speed and step-by-step memory debugging.

Company-specific guides 4

📖 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. 🎯 COMPANY GUIDE Version 1 Interview Preparation Corner Comprehensive guide covering typical interview formats, common questions, and preparation tips for Version 1 and off-campus tech roles. 📝 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. 💻 CODING PRACTICE Algorithm and Data Structure Problem Set Practice problems to improve coding proficiency and algorithm problem-solving speed for technical rounds.

Likely coding questions 4

💻 Two Sum & Two Pointers Array Optimization Easy / Medium Asked in 85% tech rounds ⚡ Live debugger 💻 Reverse Linked List & Detect Cycle (Floyd's Algorithm) Medium Frequently asked in core rounds ⚡ Live debugger 💻 SQL Nth Highest Salary with DENSE_RANK() & Subqueries Medium Top standard database question ⚡ Live debugger 💻 Valid Parentheses & Stack Implementation Easy / Medium Standard online assessment problem ⚡ Live debugger

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 (Artificial Intelligence, Stack, Full Stack, Python, Java, SQL, React, Angular) & 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 Version 1 Core Values, Learning Agility & Offer Terms Demonstrate passion, strong communication, and readiness for full-time collaboration.

Technical questions 5

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.

⚡ Practice in the code editor

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.

⚡ Practice in the code editor

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.

⚡ Practice in the code editor

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.

⚡ Practice in the code editor

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.

⚡ Practice in the code editor

Behavioural & HR questions 3

Why do you want to join Version 1 as a Full Stack AI Engineer?
Highlight Version 1's market reputation, recent tech innovations, and how your skills in Artificial Intelligence 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.

About the role

Eligibility & process

Who can apply

Skills & stack

Selection process

Prep kit

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