MLOps Engineer
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What you'll do
- Design, build, and maintain high-performance, serverless microservices and RESTful APIs for model inference and platform operations. Ensure high availability, reliability, and smooth day-to-day operations.
- Build, configure, and maintain robust CI/CD pipelines (leveraging platforms like GitHub and GitLab) using Infrastructure-as-Code to automate testing and deployment. Implement automated checks to prevent operational downtime.
- Leverage native AWS AI/ML platforms to deploy, monitor, and manage the lifecycle of Machine Learning and Generative AI models. Set up basic observability for model drift, inference latency, and performance metrics.
What they're looking for
- 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, and Cloud. Fresh graduates with strong project experience or internships in these areas are welcome to apply.
- Strong proficiency in Python and hands-on experience with FastAPI. Basic knowledge of Vue.js (or similar modern JavaScript frameworks).
- Solid understanding of core AWS services (Lambda, S3, Sagemaker, Bedrock, IAM, ECS, API Gateway, etc.) and hands-on experience with AWS CDK for defining infrastructure as code.
- Practical experience creating and managing Docker containers, utilizing version control systems ( GitHub, GitLab ), and configuring deployment pipelines (e.g., using GitHub Actions, GitLab CI/CD, or Jenkins).
- Experience working with both relational (PostgreSQL) and NoSQL (AWS DynamoDB) databases.
Nice to have
- AWS or GCP Cloud certifications (e.g., AWS Certified Cloud Practitioner / Developer, GCP Associate Cloud Engineer).
- Active GitHub profile, Kaggle participation, or personal projects demonstrating end-to-end full-stack or computer vision implementations.
- AWS services like SageMaker and Step Functions.
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
Job Description:
At Airbus, we are harnessing the power of artificial intelligence to enhance efficiency and quality across our value chain. Our team is composed of technologists and business leaders dedicated to innovation and excellence.
We are seeking a MLOps Engineer (0-2 Years of Experience) to join our Advanced Analytics and Artificial Intelligence PSL. Operating within a high-growth AI team, you will play a critical role in developing, maintaining, and scaling our centralized ModelOps platform. You will bridge software engineering, DevOps, and Machine Learning operations to ensure our AI infrastructure remains robust while independently executing feature enhancements..
Working closely with senior engineers, you will focus on enhancing the ModelOps platform, helping to build MLOps deployment pipelines for both traditional Machine Learning and Generative AI models, exploring modern ML services, automating operational workflows, and building serverless internal tooling.
Qualifications & Experience
Education: Bachelor’s or Master’s degree in Computer Science, Software Engineering, Information Technology, Data Science, or a related quantitative field.
Experience: 0–2 years of hands-on experience in Software Engineering, DevOps, MLOps, and Cloud. Fresh graduates with strong project experience or internships in these areas are welcome to apply.
Key Responsibilities
Platform Operations & APIs: Design, build, and maintain high-performance, serverless microservices and RESTful APIs for model inference and platform operations. Ensure high availability, reliability, and smooth day-to-day operations.
CI/CD & Pipeline Automation: Build, configure, and maintain robust CI/CD pipelines (leveraging platforms like GitHub and GitLab) using Infrastructure-as-Code to automate testing and deployment. Implement automated checks to prevent operational downtime.
MLOps & Model Management: Leverage native AWS AI/ML platforms to deploy, monitor, and manage the lifecycle of Machine Learning and Generative AI models. Set up basic observability for model drift, inference latency, and performance metrics.
Technical Requirements
Core Programming: Strong proficiency in Python and hands-on experience with FastAPI. Basic knowledge of Vue.js (or similar modern JavaScript frameworks).
Cloud & Infrastructure: Solid understanding of core AWS services (Lambda, S3, Sagemaker, Bedrock, IAM, ECS, API Gateway, etc.) and hands-on experience with AWS CDK for defining infrastructure as code.
DevOps & Containerization: Practical experience creating and managing Docker containers, utilizing version control systems (GitHub, GitLab), and configuring deployment pipelines (e.g., using GitHub Actions, GitLab CI/CD, or Jenkins).
Databases: Experience working with both relational (PostgreSQL) and NoSQL (AWS DynamoDB) databases.
Soft Skills & Behavioral Attributes
Fast Learning Curve: Demonstrated ability to quickly master new technologies, tools, and frameworks.
Adaptability & Agility: Willingness to wear multiple hats—building API one day and tweaking ML pipelines the next.
Problem-Solving Mindset: Strong logical reasoning, analytical skills, and attention to detail.
Communication: Clear written and verbal communication skills to articulate ideas effectively within a team environment.
Nice-to-Have / Added Advantages
AWS or GCP Cloud certifications (e.g., AWS Certified Cloud Practitioner / Developer, GCP Associate Cloud Engineer).
Active GitHub profile, Kaggle participation, or personal projects demonstrating end-to-end full-stack or computer vision implementations.
AWS services like SageMaker and Step Functions.
This job requires an awareness of any potential compliance risks and a commitment to act with integrity, as the foundation for the Company’s success, reputation and sustainable growth.
Company:
Airbus India Private LimitedEmployment Type:
Permanent-------
Experience Level:
Entry LevelJob Family:
Digital <JF-IM-DI>By submitting your CV or application you are consenting to Airbus using and storing information about you for monitoring purposes relating to your application or future employment. This information will only be used by Airbus.
Airbus is committed to achieving workforce diversity and creating an inclusive working environment. We welcome all applications irrespective of social and cultural background, age, gender, disability, sexual orientation or religious belief.
Airbus is, and always has been, committed to equal opportunities for all. As such, we will never ask for any type of monetary exchange in the frame of a recruitment process. Any impersonation of Airbus to do so should be reported to emsom@airbus.com.
At Airbus, we support you to work, connect and collaborate more easily and flexibly. Wherever possible, we foster flexible working arrangements to stimulate innovative thinking.
Company
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