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REQUIREMENT_ID_1595 • 3-DAY_ACTIVE_POLICY N

AI Full Stack Developer

Company
Company Mphasis
Type
Opportunity Type Full-Time Job
Salary
Stipend / Salary Competitive Package (Best in Industry)
Location
Location Bengaluru, India
Posted Date
Posted Date Yesterday
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Kubernetes Coding Full Stack SQL AWS Spring Boot Engineering
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Aptitude Practice Questions & Mock Tests Open Resource ↗
Curated logical, quantitative, and verbal reasoning problems for the initial online screening round.
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Company-Specific Interview Preparation Corner Open Resource ↗
Detailed interview experiences, exam formats, and previous test questions for Mphasis and tech roles.
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Technical Placement Cheat Sheets & Question Bank Open Resource ↗
High-yield coding cheat sheets, core CS fundamentals (OOP, DBMS, OS, Networks), and rapid revision guides.
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Algorithm, DSA & Live Code Debugger Practice Open Resource ↗
Hands-on problem sets to improve coding speed and step-by-step memory debugging.

Experience: 6-9 Yrs. Open to matching qualifications.

1
Online Assessment / Technical Interview Rounds / HR Review
Job Description: Senior Full Stack AI Engineer Position: Senior Full Stack AI Engineer Experience: 6+ Years Location: Bangalore (preferred), Hyderabad Joining: Immediate About the Role We are looking for highly skilled Senior Full Stack AI Engineers who can independently design, build, and deploy AI-powered applications leveraging Large Language Models (LLMs). The ideal candidate should be equally comfortable working across the full stack, from developing scalable backend services and modern frontend applications to creating AI-driven solutions and automated testing frameworks that enable developer-led quality engineering. This role requires hands-on expertise in LLM development, application architecture, and test automation, effectively helping organizations accelerate delivery while reducing dependency on traditional QA teams. Key Responsibilities Design, develop, and deploy AI-powered applications using Large Language Models (LLMs), with experience in Claude preferred. Architect and build scalable full stack applications using Java, Spring Boot, ReactJS, NextJS, TypeScript, and JavaScript . Develop intelligent workflows utilizing AI agents, prompt engineering, retrieval-augmented generation (RAG), and model integrations. Create end-to-end automated test suites using Playwright , Perfecto , and other modern automation frameworks to support developer-led testing. Build and integrate RESTful APIs and microservices following modern architecture principles. Design and manage data solutions using SQL, NoSQL, and MongoDB . Develop event-driven applications using messaging platforms such as Kafka . Implement secure, scalable, and high-performance solutions using Spring Security, OAuth, and multi-threaded programming . Leverage AWS services including ECS, Lambda, and Step Functions to build cloud-native applications. Containerize and orchestrate applications using Docker and Kubernetes . Drive quality through TDD , JUnit, SonarQube, CI/CD pipelines, and engineering best practices. Collaborate with product, design, and engineering teams across global locations to build customer-centric digital products. Participate in Agile ceremonies and contribute to architecture, design, and technical decision-making. Required Skills & Experience 5+ years of professional software engineering experience. Strong hands-on expertise in: Java, Spring Boot JavaScript, TypeScript ReactJS, NextJS HTML5, CSS3 Proven experience building and integrating AI/LLM-powered solutions. Ability to independently develop, evaluate, and optimize LLM-based applications. Strong understanding of software testing, automation frameworks, and quality engineering practices. Experience with: Playwright and/or Perfecto SQL, NoSQL, MongoDB Kafka or similar messaging platforms Maven, Gradle, Git-based CI/CD REST APIs and Microservices AWS cloud services Docker and Kubernetes Strong analytical and problem-solving skills. Excellent communication and stakeholder management abilities. Experience working in Agile and globally distributed teams. Preferred Qualifications Hands-on AI practitioner with experience building production-grade AI applications. Experience with: RAG architectures AI Agents and workflow orchestration Prompt Engineering Model evaluation and optimization Claude, OpenAI, Gemini, or comparable LLM ecosystems Strong understanding of distributed systems and low-latency applications. Experience in financial services, asset management, or fintech domains. Expertise in microservices architecture and cloud-native design patterns. Experience collaborating with UX designers and frontend specialists. Knowledge of visualization libraries such as HighCharts , D3.js , or similar frameworks. Experience delivering high-quality backend and frontend features in fast-paced environments. Familiarity with CMS-driven platforms and enterprise digital ecosystems. Ideal Candidate Profile A hands-on Full Stack AI Engineer who can build AI solutions from scratch, own end-to-end application delivery, and drive quality through automation rather than manual testing practices . The candidate should combine strong engineering fundamentals with practical AI implementation experience and be capable of delivering scalable enterprise-grade solutions independently. Please share your updated resume - Hemanth.kumars1@mphasis.com We are looking for only Immediate joiners or Max 15 Days notice period. Total Experience : Relevant Experience: Current CTC : Expected CTC : Notice Period: Current Location : Preferred Location:

Mphasis — 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.

Open Resource ↗
COMPANY GUIDE 🎯
Mphasis Interview Preparation Corner

Comprehensive guide covering typical interview formats, common questions, and preparation tips for Mphasis 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 (Kubernetes, Coding, Full Stack, SQL, AWS, Spring, Boot, Engineering) & 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
Mphasis 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 Mphasis as a AI Full Stack Developer?
Preparation Tip: Highlight Mphasis's market reputation, recent tech innovations, and how your skills in Kubernetes 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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