New opportunity

Associate Engineer - AI services

NTT DATA · Chennai, India

About this role

The Associate Engineer - AI services is an entry-level role focused on designing, developing, and testing software systems, modules, and applications, including cloud-based tools. The role involves working in a micro-services environment, managing APIs, handling telemetry data, and applying ML/AI algorithms to structured and unstructured data. Key responsibilities include assisting with detailed design, prototyping multi-vendor infrastructure, writing and testing code, executing automated tests, and participating in software deployment. The engineer will collaborate with product teams to integrate solutions and maintain technical documentation. Requirements include a Bachelor's degree in Computer Science, Engineering, or a related field, along with proficiency in programming languages such as Python, Java, C/C++, C#, and Node.js. Candidates should have a basic understanding of cloud services (AWS, GCP, Azure), container runtimes (Docker, Kubernetes), and data stores (SQL, NoSQL). Experience with CI/CD, Agile methodologies, and software delivery lifecycle tools is expected. Preferred qualifications include Microsoft Certified Azure Fundamentals and relevant Agile certifications. This is a hybrid position based in Chennai, India, offering the opportunity to work in a diverse, fast-paced environment focused on technical excellence and innovation.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Cloud ArchitectureAWSGCPMicrosoft AzureC/C++C#JavaJavaScriptPythonNode.jsData StructuresAlgorithmsMicroservicesAPI DevelopmentSQLElasticsearchMongoDBCassandraKubernetesDockerLXC/LXDAgileTest-Driven DevelopmentCI/CDMachine LearningAI Algorithms

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Software Development and Integration
The role requires active participation in the full software delivery lifecycle, including writing, testing, and refactoring code. You must be prepared to assist in building micro-services and RESTful APIs while collaborating with product teams to integrate solutions into larger distributed systems.
AI and Data Processing
A core priority is applying ML/AI algorithms to both structured and unstructured data. You will be expected to handle telemetry data and assist in the development of AI-driven modules, requiring a solid grasp of data structures and algorithmic efficiency.
Cloud and Infrastructure Proficiency
You must demonstrate a functional understanding of cloud environments (AWS, GCP, Azure) and containerization tools like Docker and Kubernetes. The role involves prototyping multi-vendor infrastructure and supporting deployments within these cloud-based ecosystems.

Likely challenges to think through

Balancing Diverse Technical Requirements
The role demands proficiency across multiple programming languages (Python, Java, C++, etc.) and various data stores (SQL, NoSQL). The challenge lies in context-switching between these technologies while maintaining high-quality code standards; prepare by reviewing your ability to adapt your coding style to different project needs.
Navigating Complex Distributed Systems
Working on large-scale distributed systems as an entry-level engineer can be overwhelming due to the complexity of micro-services. You should prepare by familiarizing yourself with how individual services communicate via APIs and how telemetry data is used to monitor system health.
Agile Collaboration in a Hybrid Environment
Success depends on taking direction from various stakeholders and working effectively within a geo-distributed team. The challenge is maintaining clear communication and documentation standards in a fast-paced, hybrid work setting; prepare by highlighting your experience with Agile tools and collaborative version control workflows.

Practice your answers

Technical practice

Microservices Architecture Principles

What to coverExplain the core benefits of using a microservices architecture compared to a monolithic approach. Be prepared to discuss how you would handle inter-service communication and maintain data consistency in a distributed environment.

Containerization with Docker and Kubernetes

What to coverDescribe the role of Docker in creating portable development environments and how Kubernetes manages container orchestration at scale. Suggestion: Familiarize yourself with basic pod lifecycles and service discovery mechanisms.

API Development and RESTful Services

What to coverWhat are the key characteristics of a RESTful API, and how do you ensure security and versioning when developing them? Focus on HTTP methods, status codes, and the importance of documentation.

SQL vs. NoSQL Data Stores

What to coverCompare the use cases for SQL databases versus NoSQL databases like MongoDB or Cassandra. Discuss how you would choose between them based on data structure, scalability requirements, and consistency needs.

CI/CD Pipeline Fundamentals

What to coverExplain the purpose of a CI/CD pipeline in the software delivery lifecycle. Suggestion: Be ready to describe how automated testing and deployment steps contribute to code quality and faster release cycles.

Applying ML/AI Algorithms

What to coverHow do you approach selecting an appropriate machine learning algorithm for a given dataset? Discuss the importance of data preprocessing and how you validate the performance of your models.

Cloud Service Models

What to coverBriefly explain the differences between IaaS, PaaS, and SaaS. Suggestion: Since the role involves multi-vendor infrastructure, be prepared to discuss how cloud-agnostic design patterns can help avoid vendor lock-in.

Test-Driven Development (TDD)

What to coverDescribe the TDD cycle and why it is beneficial for software reliability. Explain how you would write unit tests for a function before implementing the actual logic.

Behavioural practice

Collaborating in Diverse Teams

What to coverDescribe a situation where you had to work with team members from different technical backgrounds or perspectives. Focus on how you communicated your ideas and ensured the team reached a common goal.

Handling Dynamic Work Environments

What to coverProvide an example of a time you had to adapt to rapidly changing priorities or project requirements. Explain the steps you took to stay organized and maintain the quality of your work.

Taking Initiative on Technical Tasks

What to coverTell me about a time you identified a technical problem or an area for improvement and took the initiative to address it. What was the outcome, and what did you learn from the process?

Receiving and Implementing Feedback

What to coverDescribe a situation where you received constructive feedback on your code or design. How did you process the feedback, and what actions did you take to improve your output?

Sharpen your knowledge

Topics to prepare

Microservices and API Architecture
The role requires designing and developing functionality within a microservices environment. Review the principles of RESTful API design, service decomposition, and inter-service communication. Exercise: Draft a high-level design for a simple microservice that interacts with an external data source via REST APIs.
Software Delivery Lifecycle (SDLC) and CI/CD
The position involves participating in software deployment and utilizing CI/CD tools. Study the stages of a modern CI/CD pipeline, including automated testing and deployment strategies. Exercise: Map out a standard CI/CD pipeline flow from code commit to production deployment, identifying where automated testing and quality gates occur.
ML/AI Algorithm Application
The role involves applying ML/AI algorithms to structured and unstructured data. Review fundamental machine learning concepts and common libraries used in Python or Java. Exercise: Select a public dataset and outline the steps to preprocess the data and apply a basic classification or regression algorithm.

Skills to revise

Containerization with Docker and Kubernetes
The role requires a basic understanding of container runtimes. Practice creating a Dockerfile for a simple application and understand how Kubernetes manages container orchestration. Exercise: Containerize a basic Python or Node.js application and write a simple Kubernetes deployment manifest.
Database Management (SQL and NoSQL)
The job requires proficiency with SQL and NoSQL stores like MongoDB or Cassandra. Understand the trade-offs between relational and non-relational databases. Exercise: Write a SQL query to join two tables and explain a scenario where a NoSQL document store like MongoDB would be preferred over a relational database.
Test-Driven Development (TDD)
The role emphasizes a belief in TDD practices. Practice writing unit tests before implementing the actual code logic. Exercise: Implement a simple function in your preferred language (Python or Java) using a TDD approach, writing the test case first to define expected behavior.

Your preparation checklist

Demonstrate proficiency in core programming and data structures
Prepare to discuss your experience with Python, Java, or C# by creating a small project that implements a common algorithm or data structure. Be ready to explain your choice of language and how you optimized your code for performance.
Build a microservices-based API prototype
Develop a simple RESTful API using Node.js or Java that interacts with a database like MongoDB or SQL. Document how you containerized this service using Docker and how it would be deployed in a cloud environment like AWS or Azure.
Showcase familiarity with AI/ML integration
Create a brief walkthrough of a project where you applied a basic ML algorithm to a structured or unstructured dataset. Explain the data preprocessing steps you took and how you integrated the model into a software application.
Prepare for technical discussions on CI/CD and testing
Review your experience with software delivery lifecycle tools. Be prepared to explain how you have used automated testing (TDD) or CI/CD pipelines to ensure code quality and how you would handle debugging in a distributed system.
Formulate questions about the AI services team
Prepare thoughtful questions for the interviewer regarding the team's current tech stack, how they handle telemetry data, or the specific types of AI/ML projects they are currently prioritizing to demonstrate your interest in their specific domain.

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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