MLOps Engineer (0-2 Years) at Airbus, Bangalore | Apply Now

Job Description

At Airbus, we are leveraging artificial intelligence to boost efficiency and quality across our value chain. Our team brings together technologists and business leaders dedicated to innovation and excellence.

We are looking for an MLOps Engineer with 0-2 years of experience to join our Advanced Analytics and Artificial Intelligence PSL. In this high-growth AI team, you will help develop, maintain, and scale our centralized ModelOps platform. You will work at the intersection of software engineering, DevOps, and Machine Learning operations to keep our AI infrastructure robust while independently delivering feature enhancements.

Collaborating with senior engineers, you will focus on improving the ModelOps platform, building MLOps deployment pipelines for both traditional Machine Learning and Generative AI models, exploring modern ML services, automating operational workflows, and developing serverless internal tools.

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 (using platforms like GitHub and GitLab) with Infrastructure-as-Code to automate testing and deployment. Implement automated checks to prevent operational downtime.
• MLOps & Model Management: Use 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., 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 an 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.