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Machine Learning / AI Engineer IIT/NIT - Bangalore

Shell Infotech

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About the role

Machine Learning / AI Engineer - Only IIT / NIT Pass out required We are looking for a skilled Machine Learning / AI Engineer with 37 years of relevant experience to join our Data Science & AI team in Bangalore. The ideal candidate should have strong technical expertise in machine learning, model deployment, MLOps, cloud infrastructure, and database technologies. The candidate will be responsible for developing, deploying, and maintaining scalable machine learning models and production-ready AI applications. Key Responsibilities Design, develop, train, and optimize machine learning models using PyTorch and/or TensorFlow. Develop and deploy scalable AI/ML applications and APIs in production environments. Implement and maintain CI/CD pipelines to automate model testing, integration, and deployment. Apply MLOps best practices for model versioning, monitoring, deployment, and lifecycle management. Utilize Docker and Kubernetes for application containerization and orchestration. Deploy and manage machine learning models and APIs on AWS cloud infrastructure, particularly AWS Lambda and EC2. Develop and optimize SQL queries using MySQL and work with NoSQL databases. Manage large-scale document metadata and implement efficient data storage and retrieval solutions. Collaborate with data scientists, software engineers, and cross-functional teams to integrate machine learning solutions into production systems. Monitor model performance, troubleshoot deployment issues, and ensure application reliability, scalability, and efficiency. Follow software engineering best practices, including code quality, testing, documentation, and version control. Eligibility Criteri B.Tech or M.Tech in Computer Science IIT / NIT Only, Artificial Intelligence, Mathematics, Data Science, or a related technical discipline. Candidates from IITs or NITs as per the organization's hiring criteria. Experience Requirements 37 years of relevant experience in Machine Learning, Artificial Intelligence, or a related engineering domain. Practical exposure to machine learning model development and production deployment. Experience working with cloud platforms, databases, and scalable application architectures is preferred. Required Technical Skills Machine Learning: Hands-on experience with PyTorch and/or TensorFlow. MLOps & CI/CD: Knowledge of CI/CD pipelines, model deployment, and MLOps workflows. Containerization: Familiarity with Docker and Kubernetes. Cloud Technologies: Experience or working knowledge of AWS Lambda and EC2-based API deployments. Databases: Strong understanding of SQL and MySQL, along with familiarity with NoSQL databases. Production Deployment: Experience deploying, monitoring, and maintaining machine learning applications in production. Data Management: Knowledge of large-scale document metadata processing and efficient data retrieval. Compensation & Working Conditions Experience: 3- 7 Years CTC - 16โ€“30 LPA Location โ€“ Bangalore Work Mode - On Site Shift - Day Shift Working Days - 6 Days a Week Notice Period - Up to 30 Days Interview Process 1st RoundHR โ€“ Initial Screening (Virtual) 2nd Round OPS 3rd RoundClient Contact person: HR Madiha @ 9625335887

Eligibility & process

Who can apply

Experience: 3-8 Yrs. Open to matching qualifications.

Skills & stack

Algorithms SQL Scalability Testing Intelligence Mathematics Data Data Engineering Docker

Selection process

  1. Online Assessment / Technical Interview Rounds / HR Review

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 (Algorithms, SQL, Scalability Testing, Intelligence, Mathematics, Data, Data Engineering, Docker) & 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 Shell Infotech Core Values, Learning Agility & Offer Terms Demonstrate passion, strong communication, and readiness for full-time collaboration.
Technical questions 5
What is the difference between Implicit Wait, Explicit Wait, and Fluent Wait in Selenium? Answer
Model answer: Implicit Wait sets a global timeout for all element lookups. Explicit Wait pauses execution until a specific ExpectedCondition (e.g. elementToBeClickable) is met. Fluent Wait allows defining polling frequency and ignoring specific exceptions like NoSuchElementException.
Explain the Page Object Model (POM) and its advantages in Test Automation. Answer
Model answer: POM is a design pattern that creates an object repository for web UI elements. It separates test scripts from page locators, reducing code duplication and making maintenance easy when UI elements change.
How do you handle dynamic WebElements whose ID changes on page reload? Answer
Model answer: Use dynamic XPath methods like contains(), starts-with(), text(), or XPath axes (ancestor, following-sibling, parent) instead of brittle absolute paths.
What is the difference between @BeforeMethod and @BeforeClass in TestNG? Answer
Model answer: @BeforeClass runs once before the first test method in the current class, while @BeforeMethod executes before each individual test method.
How do you validate REST API response codes and JSON payload using Postman / RestAssured? Answer
Model answer: In RestAssured: given().when().get('/endpoint').then().assertThat().statusCode(200).body('status', equalTo('ACTIVE')).
Behavioural & HR questions 3
Why do you want to join Shell Infotech as a Machine Learning / AI Engineer IIT/NIT - Bangalore?
Highlight Shell Infotech's market reputation, recent tech innovations, and how your skills in Algorithms 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.
Shell Infotech 16-30 Lacs PA
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