Required Skills & Tech Stack
Application
Artificial Intelligence
Startup
Data
Python
Consultancy
Typescript
Javascript
📚 Free Study Materials (4)
📖
Aptitude Practice Questions & Mock Tests
Open Resource ↗
Curated logical, quantitative, and verbal reasoning problems for the initial online screening round.
🎯
Company-Specific Interview Preparation Corner
Open Resource ↗
Detailed interview experiences, exam formats, and previous test questions for United Hr Solutions and tech roles.
📝
Technical Placement Cheat Sheets & Question Bank
Open Resource ↗
High-yield coding cheat sheets, core CS fundamentals (OOP, DBMS, OS, Networks), and rapid revision guides.
💻
Algorithm, DSA & Live Code Debugger Practice
Open Resource ↗
Hands-on problem sets to improve coding speed and step-by-step memory debugging.
Eligibility Criteria
Experience: 8-10 Yrs. Open to matching qualifications.
Selection & Interview Process
1
Online Assessment / Technical Interview Rounds / HR Review
Job Description & Key Responsibilities
Job Title: Lead Full Stack AI Engineer Job Location: WFH / Remote Experiene: 8-10 Years About the Role We are looking for a hands-on Full Stack AI Engineer with strong software engineering skills and practical experience building Generative AI/LLM-powered applications.The ideal candidate will have experience across backend and frontend development, AI/LLM integration, APIs, databases and cloud platforms, with the ability to build scalable, production-ready AI applications. Key Responsibilities Design, develop and maintain full-stack Generative AI applications. Build robust backend systems and APIs for AI-driven products and agent-based applications. Integrate LLMs and GenAI models into scalable, production-ready applications. Develop user-facing applications using modern frontend technologies. Build reusable components and frameworks for model integration, deployment and monitoring . Implement appropriate logging, monitoring, tracing and validation for AI applications. Work with agentic AI frameworks such as LangChain, LangGraph or Semantic Kernel. Develop and integrate REST/GraphQL APIs and work with SQL/NoSQL databases. Containerise applications using Docker and deploy solutions on cloud platforms. Follow strong software engineering practices, including code reviews, testing and documentation. Collaborate with data scientists, AI engineers and other technical teams to deliver end-to-end AI solutions. Required Skills & Experience 5-6 years of professional software engineering/full-stack development experience. Strong hands-on programming experience in Python, preferably Django. Strong experience with JavaScript/TypeScript and React. Experience building backend systems, APIs and data-driven applications. Practical experience working with Generative AI, LLMs and AI-powered applications. Exposure to agentic frameworks such as LangChain, LangGraph, Semantic Kernel or similar. Strong understanding of REST/GraphQL APIs, backend architecture and data modelling. Experience with SQL and/or NoSQL databases. Hands-on experience with Docker and cloud platforms such as GCP, Azure or similar. Good understanding of software design principles and system design. Strong problem-solving and debugging skills. Good communication skills and ability to work collaboratively in a fast-paced environment. Preferred Experience taking AI/GenAI applications from development to production. Exposure to model deployment, monitoring, guardrails and LLM application security. Experience working in a consulting, product or start-up environment. Familiarity with machine learning, data science and large-scale data processing. Please expedite and send the updated resume along with requested details and confirmation of interest. Regards, Pooja Raval - Sr. Consultant / Team Leader Ms. Swati Sharma Recruitment Consultant Send CV and Reply mail to unitedtechit@uhr.co.in; Will call you for detail discussion, if your Profile is relevant.
Preparation Hub
United Hr Solutions — AI & Machine Learning Interview Guide
Previously asked questions, exam syllabus, coding benchmarks & round strategy.
📖 Previously Asked Questions & Preparation Links
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.
Open Resource ↗
COMPANY GUIDE
🎯
United Hr Solutions Interview Preparation Corner
Comprehensive guide covering typical interview formats, common questions, and preparation tips for United Hr Solutions 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 ↗
💻 Previously Asked Coding Challenges
Easy / Medium
⚡ Live Debugger
Two Sum & Two Pointers Array Optimization
Asked in 85% tech rounds
Medium
⚡ Live Debugger
Reverse Linked List & Detect Cycle (Floyd's Algorithm)
Frequently asked in core rounds
Medium
⚡ Live Debugger
SQL Nth Highest Salary with DENSE_RANK() & Subqueries
Top standard database question
Easy / Medium
⚡ Live Debugger
Valid Parentheses & Stack Implementation
Standard online assessment problem
🗺️ Round-Wise Strategy
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 (Application, Artificial Intelligence, Startup, Data, Python, Consultancy, Typescript, Javascript) & 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
United Hr Solutions Core Values, Learning Agility & Offer Terms
Demonstrate passion, strong communication, and readiness for full-time collaboration.
❓ Core Technical Questions & Answers
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).
🤝 Behavioral & HR Questions (STAR Method)
Why do you want to join United Hr Solutions as a Lead Full-Stack AI Engineer?
Preparation Tip: Highlight United Hr Solutions's market reputation, recent tech innovations, and how your skills in Application 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.
