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Senior Associate Engineer - AI Services

NTT DATA · Chennai, India

About this role

The Senior Associate Engineer - AI Services supports the design, development and testing of software systems, modules and applications, including cloud-based tools and product enhancements. The role develops microservices that use APIs and telemetry data, applying ML/AI algorithms to structured and unstructured data. This is a full-time, hybrid role in Chennai, Tamil Nādu, India.

Responsibilities

  • Support detailed module and subsystem design, prototype multi-vendor infrastructure and demonstrate solutions to internal or client audiences.
  • Write and test code, support automated testing and deployment, and deliver software components with the product team.
  • Integrate third-party software through coding and automation; help build and debug large-scale distributed systems.
  • Refactor and review code, and maintain technical, end-user and operational documentation.

Requirements

  • A bachelor's degree or equivalent in Computer Science, Engineering or a related field. The posting calls for moderate experience in software development, microservices and RESTful APIs, and work with geographically distributed teams across pilot and production phases; it does not specify a number of years.
  • Familiarity with large datasets and applying suitable ML/AI algorithms. Knowledge of public-cloud architecture, programming languages, data structures, software design, SQL and NoSQL stores, containers, CI/CD and cloud deployments is sought.
  • Experience with software-delivery lifecycle tools, Agile and Lean methods, Continuous Delivery or DevOps, and analytics-driven processes. Strong analytical, debugging, teamwork and communication abilities, initiative and attention to detail are also sought.

Preferred

Microsoft Certified Azure Fundamentals and relevant Agile certifications.

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

Skills for this role

Machine LearningAI algorithmsCloud architectureAWSGoogle Cloud PlatformMicrosoft AzureMicrosoft Office 365CC++C#JavaJavaScriptPythonNode.jsData structuresAlgorithmsSoftware designMicroservicesRESTful APIsSQLNoSQLElasticsearchMongoDBCassandraKubernetesDockerLXCLXDAgile methodologiesLean methodologiesätւ

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A focused preparation guide, built around this job’s responsibilities and requirements.

✦ AI-generated guide
Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.
01

Understand the role

What matters most

  • Build AI-enabled microservices

    The role develops microservices and APIs that use telemetry data and apply ML/AI algorithms to structured and unstructured data. Prepare to explain how data characteristics affect an algorithm choice and how the result would be exposed through an API.

  • Turn designs into working prototypes

    The posting calls for supporting module and subsystem design, prototyping multi-vendor infrastructure, and demonstrating solutions internally or to clients. Prepare a concise account of design decisions, prototype scope, and what a demonstration established.

  • Deliver and improve tested code

    Writing and testing code, supporting automated tests and deployment, refactoring, and reviewing others’ code are stated duties. Prepare to describe how a change moves through testing, review, and CI/CD without overstating ownership of those steps.

  • Integrate and debug distributed software

    The role supports third-party integration through coding and automation and helps build and debug large-scale distributed systems. Prepare to trace an issue across service boundaries and explain how you would isolate an integration fault.

  • Collaborate across delivery phases

    The posting seeks experience with geographically distributed teams across innovation, pilot, and production phases. It also calls for product-team collaboration and technical, end-user, and operational documentation. Prepare to show how decisions and handoffs stay clear across stakeholders.

Likely challenges to think through

  • Choosing an AI approach for varied data

    Preparation hypothesis, not a confirmed NTT DATA problem: telemetry may combine structured and unstructured inputs, making a suitable algorithm harder to choose than for one uniform dataset. Prepare to explain the trade-off between data preparation, model suitability, and the behavior needed from the service.

  • Diagnosing failures across vendor boundaries

    Preparation hypothesis: third-party software and distributed services can make it difficult to tell whether a fault sits in an API contract, an integration, or a service. Prepare a stepwise debugging approach that uses observable evidence before changing code.

  • Moving a prototype toward reliable delivery

    Preparation hypothesis: a quick multi-vendor demonstration and a production-ready component may require different levels of testing and operational detail. Prepare to distinguish what a prototype proves from what automated tests, deployment checks, and documentation must still cover.

03

Practice your answers

Technical practice

Suggested practice question: How would you design a microservice that exposes telemetry data through a RESTful API?

What to coverThe posting describes microservices that work with APIs and telemetry data. As preparation, explain the API contract, data flow, validation and failure handling, including how you would test the service.

Suggested practice question: How would you choose an ML or AI approach for a large dataset containing structured and unstructured data?

What to coverThe role calls for applying suitable algorithms to large datasets of both kinds. As preparation, discuss data quality, a baseline, evaluation measures and the trade-off between model performance and operational complexity.

Suggested practice question: How would you choose between SQL and NoSQL storage for a service?

What to coverThe posting names SQL and NoSQL stores, including Elasticsearch, MongoDB and Cassandra. As preparation, compare access patterns, consistency, scaling and search needs rather than assuming one store fits every dataset.

Suggested practice question: How would you prototype a module that must work across multi-vendor infrastructure?

What to coverThe role supports detailed module design and multi-vendor prototyping. As preparation, describe how you would define interfaces, isolate vendor-specific behaviour and demonstrate that the prototype meets its intended use.

Suggested practice question: How would you integrate third-party software into an existing service?

What to coverThe posting calls for integration through coding and automation. As preparation, cover interface compatibility, error handling, automated checks and how you would investigate an integration failure.

Suggested practice question: How would you debug a failure in a large-scale distributed system?

What to coverThe role includes helping build and debug distributed systems and working with telemetry. As preparation, explain how you would narrow the fault across components, use available diagnostic data and verify a fix without assuming the first symptom is the root cause.

Suggested practice question: How would you test a new software component before deployment?

What to coverThe posting mentions writing and testing code, automated testing, test-driven development and lifecycle tools such as mocking. As preparation, distinguish focused component tests from integration tests and explain how test results would inform a release decision.

Suggested practice question: What would you check when deploying a containerised service through CI/CD?

What to coverThe role calls for developing knowledge of containers, CI/CD and cloud deployments. As preparation, discuss build and test checks, configuration, deployment verification and how you would respond if the deployed component failed.

Suggested practice question: How would you approach refactoring code that other developers maintain?

What to coverThe posting includes refactoring and reviewing others’ code, alongside software design and data-structure knowledge. As preparation, explain how you would identify the design issue, preserve behaviour with tests and weigh clarity against the risk of a larger change.

Suggested practice question: What documentation would you update after changing a service?

What to coverThe role includes maintaining technical, end-user and operational documentation. As preparation, distinguish what each audience needs and explain how you would keep instructions aligned with the tested behaviour.

Behavioural practice

Suggested practice question: Tell me about a time you coordinated delivery with a geographically distributed team.

What to coverThe posting calls for work with distributed teams and multiple stakeholders across pilot and production phases. Use a real situation, your coordination actions and the result; make clear how you handled differing expectations or handoffs.

Suggested practice question: Tell me about a time you demonstrated a technical prototype to others.

What to coverThe role includes showcasing multi-vendor prototypes internally or to clients. Use a real example to explain the audience’s needs, what you chose to demonstrate and what resulted from their feedback.

Suggested practice question: Tell me about a time you took initiative when a development task was unclear.

What to coverThe posting values initiative and describes receiving instructions from various stakeholders. Describe the situation, how you clarified the goal and acted within your responsibility, and the resulting progress.

Suggested practice question: Tell me about a time you found a consequential defect under time pressure.

What to coverThe role calls for debugging, attention to detail and working in a fast-paced environment. Give the situation, how you investigated and communicated the issue, and the outcome without overstating your individual contribution.

Suggested practice question: Tell me about a time a code review or product-team discussion changed your approach.

What to coverThe posting includes reviewing code and collaborating with the product team. Use a real situation to show how you handled differing views, what action you took and how it affected the delivered component.

02

Sharpen your knowledge

Topics to prepare

  • Microservices and RESTful API design

    Posting: moderate experience developing microservices and RESTful APIs is requested. Revision exercise: sketch two services, their endpoints, request and response formats, and how they handle a failed call between services.

  • ML/AI choices for telemetry data

    Posting: the role runs ML/AI algorithms on telemetry and works with structured and unstructured data; familiarity with large datasets is requested. Revision exercise: compare two suitable algorithm approaches for a sample telemetry problem and explain the data preparation, evaluation measure, and trade-offs. The sample problem is a preparation suggestion, not an assigned project.

  • Cloud and container architecture

    Posting: developing understanding of public-cloud architecture, cloud deployments, and container runtimes is sought. Revision exercise: diagram how an API service, its data store, and a container would be deployed in one of the listed public clouds; explain where scaling and failures would be handled.

  • Data structures and storage selection

    Posting: data structures, algorithms, software design, SQL, and NoSQL stores are named. Revision exercise: choose a data structure for processing incoming telemetry and compare a SQL store with one listed NoSQL store for querying the results.

  • Software delivery and team practices

    Posting: Agile and Lean methods, CI/CD, test-driven development, and work across pilot and production phases with geographically distributed teams are mentioned. Revision exercise: map a small change from work item through tests, review, deployment, and defect follow-up; identify what would need communicating across teams.

Skills to revise

  • Implement and test an API component

    Posting: the role contributes code, tests, and microservice functionality. Suggested practice: use one language listed in the posting to implement a small RESTful endpoint, then write tests for valid input, invalid input, and a dependency failure. No particular framework is specified.

  • Process mixed-format telemetry

    Posting: microservices use APIs and telemetry data with ML/AI algorithms on structured and unstructured data. Suggested practice: write a small processing routine for sample records and free-text fields, handle missing values, and explain how its output could be passed to an algorithm.

  • Debug a distributed failure

    Posting: the role helps build and debug large-scale distributed systems and needs analytical debugging skills. Suggested practice: trace a simulated failed request across two services, isolate whether the fault is in an API response or data access, and document the fix and a regression test.

  • Exercise the delivery workflow

    Posting: automated testing, deployment, CI/CD, source control, and code review are part of the work. Suggested practice: take a small code change through source control, automated tests, a review checklist, and a written deployment and rollback plan; the posting does not mandate a particular pipeline tool.

  • Integrate and document a component

    Posting: the role supports third-party integration and maintains technical, end-user, and operational documentation. Suggested practice: define an interface for a sample third-party response, test its failure handling, and write brief usage and troubleshooting instructions. The sample integration is practice, not a stated employer project.

04

Your preparation checklist

  • Build a role-evidence sheet

    Map the posting’s requirements—software development, microservices, RESTful APIs, cloud deployment, large datasets and distributed teamwork—to specific examples you can discuss. Note where your experience is limited rather than filling gaps with assumptions.

  • Prepare a microservices project walkthrough

    Select a real example, if available, and sketch its API contracts, service boundaries, data stores and your contribution. Be ready to explain design decisions, debugging and the software’s path from development to deployment.

  • Practice a telemetry-and-ML design exercise

    As preparation, sketch a microservice that accepts telemetry through an API and applies an ML or AI method to structured or unstructured data. Explain data handling, algorithm choice, testing and limitations; this is a practice scenario, not a stated company project.

  • Rehearse a cloud integration design

    Diagram how you would prototype a module using third-party software and cloud infrastructure, then demonstrate it to a product team or client. Identify assumptions, integration risks and what you would validate before production.

  • Collect delivery and code-quality examples

    Prepare concise examples of code you wrote or reviewed, a refactor or distributed-system bug you investigated, and how automated tests, source control and CI/CD supported delivery. Include the outcome and your own role.

  • Prepare a collaboration and documentation example

    Outline how you worked with stakeholders or a geographically distributed team across pilot or production phases. Bring an example of a technical explanation, end-user guidance or operational procedure you helped produce, if available.

  • Write focused questions for the employer

    Ask which APIs, telemetry sources and data types the team works with; which cloud and deployment tools it uses; and how responsibilities are divided across design, testing, deployment and product-team collaboration. Clarify the Chennai hybrid arrangement if relevant.

AI-generated from the job information · Prepared Sep 30, 2026 (UTC)Useful preparation, not a guarantee of eligibility or an offer.

Availability last checked: 4 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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