NTT DATA MLOps Engineer Jobs 2026 | Python, AI & Cloud Engineers Apply

June 8, 2026 Admin

NTT DATA MLOps Engineer Jobs 2026 | Python, AI & Cloud Engineers Apply

NTT DATA is hiring for the position of MLOps Engineer. This opportunity is ideal for Computer Science and Engineering graduates who want to build careers in Machine Learning Operations (MLOps), Cloud Computing, Artificial Intelligence, Software Engineering, and DevOps. Candidates with knowledge of Python, cloud platforms, APIs, microservices, and modern software development practices can benefit from working with one of the world’s leading technology and digital transformation companies.

As part of NTT DATA’s Digital Design and Development team, selected candidates will contribute to AI-powered applications, cloud-native solutions, microservices architecture, and large-scale software systems. The role offers exposure to cutting-edge technologies including Kubernetes, Docker, AWS, Azure, GCP, Machine Learning workflows, and enterprise software development, making it an excellent opportunity for ambitious technology professionals.

Quick Job Snapshot

Company Name	NTT DATA
Role	MLOps Engineer
Qualification	Bachelor’s Degree in Computer Science, Engineering or Related Field
Experience	Early Career / Moderate Experience
Eligible Batch	2023, 2024, 2025 & Recent Graduates
Salary	Estimated ₹8 LPA – ₹15 LPA
Job Type	Full Time
Work Mode	On-site

Role Overview

The MLOps Engineer role focuses on designing, developing, testing, and deploying software systems that integrate cloud infrastructure, machine learning solutions, APIs, and distributed systems. Engineers work closely with development teams to support AI-driven applications and automate deployment processes across modern cloud environments.

The position involves working with microservices architectures, telemetry data, machine learning algorithms, cloud deployments, CI/CD pipelines, and scalable software systems. Candidates will gain valuable exposure to enterprise-level engineering practices while collaborating with global teams and contributing to innovative digital transformation initiatives.

Key Responsibilities

Design and develop software solutions that support business growth.
Contribute to coding, testing, debugging, and software deployment activities.
Develop functionality in microservices-based environments.
Work with APIs, telemetry data, and machine learning algorithms.
Support cloud-native application development and deployment.
Participate in automated testing and CI/CD workflows.
Build and integrate solutions using automation and third-party tools.
Support large-scale distributed systems and cloud infrastructure.
Maintain technical documentation and operational procedures.
Contribute to code reviews and software quality improvements.
Collaborate with product teams and software engineers.
Assist in refactoring and optimizing existing systems.

Required Skills / Eligibility

Bachelor’s degree in Computer Science, Engineering, or a related field.
Strong programming knowledge in Python, Java, JavaScript, C++, C#, or Node.js.
Understanding of cloud platforms such as AWS, Azure, or Google Cloud.
Knowledge of data structures, algorithms, and software design principles.
Experience with SQL and NoSQL databases.
Understanding of microservices architecture and REST APIs.
Knowledge of Docker, Kubernetes, and container technologies.
Familiarity with CI/CD concepts and DevOps practices.
Understanding of Agile and Lean development methodologies.
Strong analytical and debugging skills.
Ability to work effectively in collaborative environments.

Salary Insights

NTT DATA has not officially disclosed compensation details for this role. Based on industry trends and similar MLOps, Cloud Engineering, and AI-focused software development positions, candidates can expect an estimated annual package ranging from ₹8 LPA to ₹15 LPA. Compensation may vary depending on technical skills, certifications, project experience, and interview performance.

Career Growth & Future Opportunities

MLOps is one of the fastest-growing domains in the technology industry because organizations increasingly need professionals who can bridge the gap between software engineering, cloud infrastructure, and machine learning systems. Within the next 2–3 years, candidates can progress into roles such as Machine Learning Engineer, MLOps Specialist, Cloud Engineer, DevOps Engineer, AI Platform Engineer, Site Reliability Engineer (SRE), or Solutions Architect. Companies are investing heavily in AI deployment, model monitoring, automation, and cloud-native technologies, creating significant demand for skilled professionals. Candidates should focus on strengthening Python programming, cloud architecture, Kubernetes, Docker, CI/CD pipelines, data engineering, and machine learning deployment skills. Developing expertise in AWS, Azure, MLOps frameworks, infrastructure automation, and AI platforms can significantly accelerate career growth and increase earning potential in both domestic and international markets.

Recommended Skills & Learning Resources

To excel as an MLOps Engineer, candidates need a combination of software engineering, cloud computing, and machine learning skills. Python remains the most important programming language for AI and automation workflows, while cloud platforms such as AWS are widely used for deploying scalable applications. Understanding data science concepts can help professionals work more effectively with machine learning teams and production models. Candidates should also learn containerization, infrastructure automation, CI/CD pipelines, APIs, monitoring tools, and distributed systems. Since modern organizations increasingly deploy AI solutions in cloud environments, professionals who can manage the complete lifecycle from model development to production deployment are highly valued. Continuous learning and practical project experience are essential for staying competitive in this rapidly evolving field.

Interview Preparation

Candidates should prepare for questions related to Python programming, cloud architecture, APIs, databases, DevOps concepts, containerization, and machine learning fundamentals. Interviewers may evaluate understanding of Docker, Kubernetes, CI/CD pipelines, distributed systems, microservices, and software engineering best practices. Since MLOps roles often require collaboration across development and data science teams, strong communication and problem-solving abilities are equally important. Candidates should also review machine learning workflows, deployment strategies, model monitoring concepts, and cloud services offered by AWS, Azure, and GCP. Practical project experience can significantly strengthen interview performance. To improve your preparation, review AI & ML Engineer Interview Questions, Top AI Skills Freshers Must Learn, and Complete Interview Guide for Freshers 2026.

Related Career Opportunities

How to Apply for NTT DATA MLOps Engineer Jobs 2026

Click the Apply Now button below.
Visit the official NTT DATA careers portal.
Review the complete job description and eligibility requirements.
Prepare an updated resume highlighting cloud, AI, DevOps, and programming skills.
Complete the online application form.
Upload your resume and required information.
Submit the application and monitor your email for further recruitment updates.

Frequently Asked Questions (FAQs)

Who can apply for the NTT DATA MLOps Engineer role?

Candidates with a Bachelor’s degree in Computer Science, Engineering, or related disciplines and knowledge of software development, cloud technologies, and programming can apply.

Which programming languages are preferred?

NTT DATA mentions Python, Java, JavaScript, Node.js, C++, and C# among the preferred technologies. Python is particularly valuable for AI and MLOps workflows.

Are cloud skills important for this role?

Yes. Understanding cloud platforms such as AWS, Azure, and Google Cloud is highly valuable because the role involves cloud infrastructure and deployment activities.

Apply Now – NTT DATA

Final Thoughts

The NTT DATA MLOps Engineer role is an excellent opportunity for candidates who want to build careers in Artificial Intelligence, Cloud Computing, DevOps, and Software Engineering. The position offers exposure to enterprise-scale technologies, cloud platforms, AI-driven solutions, and modern software development practices. Continue strengthening your technical skills, building practical projects, and preparing for interviews to maximize your success in the rapidly growing AI and cloud technology landscape.