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Tier-1 employer, metro location, and broad MLOps skillset create high applicant competition.
Blends ML and cloud operations so candidates from devops, cloud, or data backgrounds are moderately transferable.
Explicit 0–2 years plus specific AWS, Python, Docker, and CI/CD requirements make screening moderately strict.
Develop, maintain, and scale Airbus' centralized ModelOps platform ensuring robustness and operational reliability.
Build and automate CI/CD pipelines and MLOps deployment workflows for traditional Machine Learning and Generative AI models using AWS AI/ML services.
Design high-performance serverless microservices and RESTful APIs for model inference and platform operations with a focus on uptime and observability.
Education: Bachelor's or Master's degree in Computer Science, Software Engineering, IT, Data Science, or related quantitative field.
Experience: 0-2 years in Software Engineering, DevOps, MLOps, and Cloud (including internships or projects for fresh graduates).
Technical skills: Proficiency in Python, FastAPI; experience with AWS core services (Lambda, S3, Sagemaker, Bedrock, IAM, ECS, API Gateway) and AWS CDK; containerization with Docker; CI/CD pipeline tools (GitHub Actions, GitLab CI/CD, Jenkins).
Databases knowledge: Experience with both relational (PostgreSQL) and NoSQL (AWS DynamoDB) databases.
Comfortable working across software engineering, DevOps, and ML operations in a fast-evolving AI environment.
Experience or strong interest in deploying and managing Machine Learning and Generative AI models on AWS platforms.
Able to autonomously enhance infrastructure by building serverless tools, APIs, and automating MLOps workflows in collaboration with senior engineers.