NTT DATA
Technical Engineer
NTT Data is Hiring Technical Engineer – an exciting opportunity for skilled professionals to join a global leader in IT services. This role offers involvement in advanced AI technologies, cloud infrastructure, and technical support within enterprise environments. If you’re aiming to build your career with cutting-edge tools like DataRobot and Azure Kubernetes Services (AKS), this could be the perfect fit.
About NTT Data
NTT Data is a leading IT service provider and global innovator, operating in over 50 countries. Headquartered in Tokyo, Japan, the company delivers consulting, cloud computing, digital solutions, and system integration services. NTT Data serves diverse sectors such as banking, healthcare, manufacturing, telecommunications, and public sectors. The company emphasizes innovation, efficiency, and reliable digital transformation for businesses.
Job Role: Technical Engineer
NTT Data is Hiring Technical Engineer for a strategic role focused on supporting DataRobot AI platforms in a cloud environment. The selected candidate will provide deep technical expertise, especially in deploying and managing AI models within Azure using Kubernetes. Technical Engineer
Key Responsibilities
Technical Expertise
- Provide in-depth technical guidance on DataRobot AI platforms.
- Collaborate with AI engineers and data scientists.
- Troubleshoot complex configuration and deployment issues.
Deployment & Model Handling
- Onboard existing SAS and Python-based models to DataRobot v8.0.21.
- Ingest LST report files from SAS.
- Generate monitoring outputs from historical data.
Dashboard Comparison & Visualization
- Compare current Power BI dashboards with DataRobot outputs.
- Create new visualizations within the DataRobot platform.
Upgrade & Configuration
- Lead the upgrade from DataRobot v8 to v11 on Azure.
- Configure required Azure services: AKS, networking, storage.
Debugging & Documentation
- Troubleshoot AKS container setups.
- Document procedures: user guides, best practices, and technical manuals.
Support & Training
- Deliver internal training sessions.
- Work through Shield process and manage Jira stories across environments.
Required Skills & Qualifications
- Experience with Kubernetes (preferred: Azure Kubernetes Service).
- Knowledge of Azure DevOps (ADO), platform configuration, and deployment.
- Proficiency in Python.
- Understanding of SAS model integration.
- Familiarity with Power BI for metric visualization.
- Ability to independently resolve technical issues.
- Strong written and verbal communication skills. Technical Engineer
Preferred Qualifications
- Experience in regulated industries like banking or finance.
- Prior work on governance frameworks such as Shield process.
- Exposure to AI/ML lifecycle management and MLOps practices.
Why Join NTT Data?
- Work in a technology-driven, global environment.
- Engage in cloud-based AI and data engineering.
- Contribute to projects that impact large enterprise systems.
- Competitive compensation and professional development support.
Application Process
Visit the official NTT Data careers page to submit your application. Prepare a detailed resume highlighting experience with DataRobot, Azure, Kubernetes, and Python development. Shortlisted candidates will be contacted for interviews.
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Top 10 Interview Questions With Answers
1. What is DataRobot and how have you used it?
Answer: DataRobot is an automated machine learning platform. I used it to onboard Python and SAS models, monitor performance, and integrate outputs with Power BI.
2. What are the key components of Azure Kubernetes Service (AKS)?
Answer: AKS consists of nodes, pods, deployments, storage volumes, and networking resources. I use AKS to deploy and manage containerized models for scalable AI applications.
3. How do you handle LST report files from SAS?
Answer: I parse LST files, extract model metrics, and upload the data into DataRobot to match expected monitoring metrics. Technical Engineer
4. Explain your experience with upgrading DataRobot versions.
Answer: I led a version upgrade from DataRobot v8 to v11, coordinating with cloud infrastructure teams, ensuring zero downtime, and validating post-upgrade performance.
5. How do you debug container issues in AKS?
Answer: I use Azure CLI and kubectl commands to inspect pods, logs, and events. I identify configuration mismatches and resource allocation issues.
6. What metrics do you monitor for AI model performance?
Answer: Key metrics include accuracy, F1-score, precision, recall, and model drift. These are compared with Power BI visuals for stakeholder reporting.
7. Describe the Shield process.
Answer: The Shield process ensures secure, compliant deployment of models. It includes reviews, approvals, and testing across PreDev, PostDev, Prod, and RTx environments.
8. How do you ensure consistency in model outputs across platforms?
Answer: I validate outputs between Power BI dashboards and DataRobot using historical data, aligning metrics and visuals.
9. Which scripting tools do you use for automation?
Answer: Primarily Python scripts for data preprocessing and automation. Bash scripting is used for container and server operations.
10. How do you manage Jira for environment tracking?
Answer: I manage user stories, tasks, and bug reports in Jira. I update statuses across environments and collaborate with teams for progress tracking.
Final Thoughts
NTT Data is Hiring Technical Engineer for a position that combines AI, cloud, and model deployment expertise. This job offers growth for engineers looking to strengthen their technical capabilities in AI platforms like DataRobot and infrastructure tools like Azure and Kubernetes. Apply today and build a career in a future-ready tech environment.
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