Airbus MLOps Engineer Jobs 2026 | Freshers

September 22, 2026 Admin

& 0–2 Years | BangaloreAirbus MLOps Engineer Jobs 2026 | Freshers

Airbus India Private Limited is hiring an MLOps Engineer for its Advanced Analytics and Artificial Intelligence team in the Bangalore Area. This is a permanent, entry-level position for candidates with 0–2 years of experience in software engineering, DevOps, MLOps, or cloud technologies. Fresh graduates with strong projects or internships in these areas are also welcome to apply.

The role focuses on developing, maintaining, and scaling a centralized ModelOps platform while supporting deployment pipelines for traditional Machine Learning and Generative AI models. Candidates will work across Python, FastAPI, AWS, Docker, CI/CD, Infrastructure-as-Code, PostgreSQL, DynamoDB, serverless technologies, and ML model lifecycle management.

Quick Job Snapshot

Company	Airbus India Private Limited
Job Role	MLOps Engineer
Location	Bangalore Area, Karnataka, India
Qualification	Bachelor’s or Master’s in Computer Science, Software Engineering, IT, Data Science, or related quantitative field
Experience	0–2 Years
Eligible Batch	Not specified; fresh graduates with relevant projects/internships may apply
Salary	Not disclosed
Job Type	Permanent, Full Time
Work Mode	Not specifically stated; Airbus mentions flexible working arrangements where possible
Experience Level	Entry Level
Job Requisition ID	JR10442960
Job Family	Digital

MLOps Engineer – Role Overview

The MLOps Engineer will work within Airbus’s Advanced Analytics and Artificial Intelligence PSL, helping operate and enhance a centralized ModelOps platform. The position bridges software engineering, DevOps, cloud infrastructure, and Machine Learning operations, with a focus on keeping AI infrastructure reliable and scalable.

The role also involves building MLOps deployment pipelines for both traditional ML and Generative AI models, exploring modern ML services, automating operational workflows, and developing serverless internal tooling. Candidates should therefore be comfortable moving between application development, cloud infrastructure, deployment automation, and machine learning operations.

Key Responsibilities

Design, build, and maintain high-performance serverless microservices and RESTful APIs for model inference and platform operations.
Support high availability, reliability, and smooth day-to-day platform operations.
Build, configure, and maintain CI/CD pipelines using platforms such as GitHub and GitLab.
Use Infrastructure-as-Code to automate testing and deployment processes.
Implement automated checks designed to prevent operational downtime.
Use native AWS AI/ML platforms to deploy, monitor, and manage Machine Learning and Generative AI models.
Set up basic observability for model drift, inference latency, and performance metrics.
Work with senior engineers while independently executing feature enhancements.
Explore modern ML services and automate operational workflows.
Build serverless internal tools supporting the ModelOps platform.

Technical Requirements & Eligibility

Bachelor’s or Master’s degree in Computer Science, Software Engineering, Information Technology, Data Science, or a related quantitative field.
0–2 years of hands-on experience in Software Engineering, DevOps, MLOps, or Cloud.
Fresh graduates with strong projects or internships in relevant areas are welcome to apply.
Strong proficiency in Python.
Hands-on experience with FastAPI.
Basic knowledge of Vue.js or a similar modern JavaScript framework.
Understanding of AWS services including Lambda, S3, SageMaker, Bedrock, IAM, ECS, and API Gateway.
Hands-on experience with AWS CDK for Infrastructure-as-Code.
Practical experience creating and managing Docker containers.
Experience with GitHub, GitLab, or similar version control systems.
Experience configuring deployment pipelines using GitHub Actions, GitLab CI/CD, Jenkins, or comparable tools.
Experience with relational databases such as PostgreSQL.
Experience with NoSQL databases such as AWS DynamoDB.

Soft Skills & Behavioral Attributes

Ability to learn new technologies, tools, and frameworks quickly.
Adaptability when working across APIs, ML pipelines, cloud infrastructure, and software development.
Strong logical reasoning and analytical skills.
Attention to detail and a practical problem-solving mindset.
Clear written and verbal communication.
Ability to collaborate effectively within a team environment.

Nice-to-Have Skills

AWS or GCP cloud certifications such as AWS Certified Cloud Practitioner, AWS Certified Developer, or GCP Associate Cloud Engineer.
Active GitHub profile demonstrating technical projects.
Kaggle participation or data science/ML project experience.
End-to-end full-stack or computer vision implementations.
Additional AWS experience with SageMaker and Step Functions.

Salary Insights

Airbus has not disclosed the salary for this MLOps Engineer position in the provided listing. Therefore, no specific compensation figure should be treated as official. Salary can vary based on experience, technical skills, location, and the company’s compensation structure.

Because this position combines software engineering, cloud, DevOps, and MLOps responsibilities, candidates should evaluate the opportunity based on the complete role scope and official compensation offered during the recruitment process.

Why This Role Is Relevant for MLOps Candidates

This position combines several technical areas that are commonly used in modern AI production environments. Candidates can work across Python, FastAPI, AWS, Docker, CI/CD, Infrastructure-as-Code, databases, serverless architecture, ML deployment, model monitoring, and Generative AI. The opportunity is also explicitly open to fresh graduates who can demonstrate relevant project or internship experience.

A strong GitHub portfolio showing an end-to-end project—such as a machine learning API deployed on AWS with Docker, CI/CD, monitoring, and database integration—can help candidates demonstrate practical understanding of the technologies mentioned in the job description.

Recommended Skills & Learning Resources

Candidates preparing for this role should prioritize Python, cloud fundamentals, machine learning concepts, APIs, databases, Docker, CI/CD, and AWS. Building one complete project that combines these areas can be particularly useful.

Python Course and Notes – useful for strengthening Python fundamentals and scripting.
AWS Course – helpful for learning cloud services relevant to the role.
Data Science Complete Course – useful for ML and data science fundamentals.

Interview Preparation

Prepare for questions covering Python, FastAPI, REST APIs, AWS services, Docker, Git, CI/CD, Infrastructure-as-Code, PostgreSQL, DynamoDB, machine learning deployment, model monitoring, and basic Generative AI concepts. Be prepared to explain a project end-to-end, including how you trained or used a model, exposed it through an API, containerized the application, deployed it to cloud infrastructure, and monitored its performance.

AI & ML Engineer Interview Questions and Answers and Advanced DevOps Interview Questions can help with technical preparation.

Related Jobs

How to Apply for Airbus MLOps Engineer

Review the educational and 0–2 years experience requirements.
Check your knowledge of Python, FastAPI, AWS, Docker, Git, CI/CD, databases, and MLOps.
Update your resume with relevant software, cloud, ML, DevOps, or MLOps projects.
Include your GitHub profile, Kaggle work, certifications, or project portfolio if relevant.
Open the official Airbus careers application page using the Apply Now button below.
Complete the application with accurate education, experience, and contact information.
Submit your application and monitor your registered email for further recruitment communication.

Apply Now

Frequently Asked Questions

1. Can freshers apply for the Airbus MLOps Engineer position?

Yes. The position specifies 0–2 years of experience and explicitly states that fresh graduates with strong project experience or internships in Software Engineering, DevOps, MLOps, or Cloud are welcome to apply.

2. What skills are required for the Airbus MLOps Engineer role?

Important requirements include Python, FastAPI, AWS services, AWS CDK, Docker, GitHub or GitLab, CI/CD pipelines, PostgreSQL, DynamoDB, and MLOps concepts. Basic Vue.js or another modern JavaScript framework is also mentioned.

3. Is the salary mentioned for this Airbus job?

No salary figure is disclosed in the provided listing. Candidates should confirm the compensation offered during the official recruitment process.

Final Takeaway

The Airbus MLOps Engineer position in Bangalore is an entry-level permanent opportunity for candidates with 0–2 years of experience and relevant project or internship exposure. Its technical scope spans Python, FastAPI, AWS, Docker, CI/CD, Infrastructure-as-Code, databases, ML model deployment, and Generative AI. Candidates who can demonstrate practical MLOps or cloud projects should review the requirements and apply through the official Airbus careers portal.

WhatsApp Version

🏢 Company: Airbus
💼 Role: MLOps Engineer
🎓 Qualification: Bachelor’s/Master’s – CS, Software Engineering, IT, Data Science or related field
📅 Batch: Not Specified | Freshers with relevant projects/internships can apply
💰 Salary: Not Disclosed