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Associate AI & LLM Systems Engineer

Electro Global

  • Jaipur, India
  • Competitive Package
  • Fresher (0-1 Yrs)
  • Posted Today
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About the role

About ElectroGlobal Solutions

ElectroGlobal Solutions Private Limited

Jaipur, Rajasthan

  • Deep-Tech Product Company

ElectroGlobal Solutions Private Limited is a rapidly growing deep-tech product company specializing in Robotics, Semiconductor Technologies, Artificial Intelligence, Embedded Systems, Internet of Things (IoT), Industrial Automation, and Intelligent Product Development.

Recognized under the Startup India initiative and supported through the innovation ecosystem of the Government of Rajasthan, ElectroGlobal is committed to developing next-generation technology products that solve real-world industrial and business challenges. The company leverages startup ecosystem support from Microsoft and Amazon Web Services (AWS).

Role Overview

ElectroGlobal Solutions is seeking highly motivated Associate AI & LLM Systems Engineers to join our Artificial Intelligence division.

This role is designed for recent graduates and early-career professionals passionate about Artificial Intelligence, Machine Learning, Generative AI, Large Language Models (LLMs), Agentic AI Systems, and Intelligent Automation.

Key Responsibilities

Key Responsibilities

  • Assist in developing AI-powered applications and intelligent software systems
  • Support integration of Large Language Models into business solutions
  • Develop prompt engineering strategies for AI solutions
  • Support development of Retrieval-Augmented Generation (RAG) systems
  • Prepare datasets and assist in model training, evaluation, and deployment

Technologies & Skills

Required Technical Skills

  • Python, SQL (basic), JavaScript (preferred)
  • Machine Learning Fundamentals, Generative AI Basics, LLM Fundamentals, Prompt Engineering
  • API Integration, Data Processing, AI Workflow Development

Preferred Technologies

  • OpenAI APIs, LangChain, Hugging Face
  • Vector Databases, RAG Systems
  • FastAPI, Streamlit, Git & GitHub

Perks & What We Offer

What We Offer

  • Competitive compensation package
  • AI certification sponsorship
  • Research & innovation exposure
  • Professional mentorship
  • Emerging technology projects

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Eligibility & process

Who can apply

Employment type: Full-time

Skills & stack

Python JavaScript SQL Next.js FastAPI AWS Git Machine Learning

Selection process

Details are shared by the recruiter after shortlisting.

Prep kit

20 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, SQL, Next.js, FastAPI, AWS, Git, Machine Learning) & 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 Electro Global 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.
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.
Behavioural & HR questions 3
Why do you want to join Electro Global as a Associate AI & LLM Systems Engineer?
Highlight Electro 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.
Electro Global Competitive Package
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