Job Description:

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 looking for an experienced, hands-on Data Scientist (Computer Vision) to lead the end-to-end development of vision-based AI solutions. In this role, you will bridge the gap between complex business challenges and cutting-edge Computer Vision technologies.

You will take full ownership of the CV lifecycle—from defining annotation strategies and curated dataset pipelines, to architecting state-of-the-art deep learning models, deploying them to cloud infrastructure, and measuring their real-world business impact. You will also collaborate closely with cross-functional business stakeholders and guide junior/full-stack developers in building scalable AI systems.

Qualification & Experience: 

Education: Master’s or Bachelor’s degree in Computer Science, Data Science, Electrical Engineering, Mathematics, or a related quantitative field.

Experience: 5+ years of hands-on experience in Data Science and Machine Learning, with at least 3+ years dedicated specifically to solving Computer Vision applications in production settings.

Key Responsibilities 

End-to-End Model Lifecycle Development: Design, build, train, evaluate, and optimize custom Computer Vision models (classification, object detection, segmentation, tracking, OCR, visual inspection) from concept to production.

Data Pipeline & Annotation Strategy: Establish data collection, cleaning, and labeling pipelines; evaluate and leverage annotation platforms (e.g., CVAT, Labelbox); define guidelines to ensure high-quality training datasets.

Business Problem Translation: Partner directly with business leaders and product teams to translate ambiguous business requirements into practical, well-scoped Computer Vision problems with clear KPIs.

Cloud Architecture & Deployment: Architect scalable ML pipelines on cloud platforms (AWS or GCP) using containerization and serverless/managed machine learning services.

Model Optimization & MLOps: Quantize, compress, and optimize models (e.g., using ONNX, TensorRT, OpenVINO) for low-latency inference on cloud or edge environments; set up monitoring for model drift and performance.

Technical Leadership & Mentorship: Guide full-stack/ML engineers on best practices in model design, code quality, research methodology, and experimentation tracking.

Continuous Innovation: Stay up-to-date with recent advancements in Computer Vision, Vision-Language Models (VLMs), and modern AI architectures to evaluate buy-vs-build options and bring novel ideas to the team.

Technical Essentials

• Deep Learning & Frameworks: Expert proficiency in Python and core deep learning frameworks (PyTorch or TensorFlow/Keras).

• Computer Vision Ecosystem: Camera fundamentals, basics of image and video encoding, working knowledge of Camera calibration, 3d reconstruction and Multi camera multi object tracking. Experience using CV tools and frameworks like OpenCV, Nvidia Deepstream, Colmap and DL architectures for CV tasks like object localization, feature extraction and matching and foundation models.

• Cloud Proficiency (AWS or GCP):

  • AWS Stack: SageMaker, S3, EC2, Lambda, Rekognition, ECR/EKS. OR

  • GCP Stack: Vertex AI, Google Cloud Storage, Cloud Run, Vision API, Compute Engine.

• Data Engineering & Tooling: Hands-on experience with dataset versioning (e.g., DVC), annotation management, and relational/NoSQL databases.

• Software Engineering & MLOps: Proficiency in standard software practices—Git, unit testing, modular coding, Docker, REST API design (FastAPI/Flask), and ML experiment tracking tools (e.g., MLflow, Weights & Biases).

Soft Skills & Business Essentials

• Business Acumen: Ability to link technical metrics (e.g., mAP, IoU, F1-score) directly to business outcomes (e.g., operational efficiency, cost reduction, accuracy thresholds).

• Stakeholder Management: Outstanding verbal and written communication skills to present technical findings clearly to non-technical business leaders.

• Problem-Solving Mindset: A structured, analytical approach to troubleshooting complex edge cases in unstructured visual data.

Nice-to-Have / Added Advantages

• Professional AWS (e.g., AWS Certified Machine Learning - Specialty) or GCP (e.g., Google Professional Machine Learning Engineer) certifications.

• Experience with Generative AI for Vision (Diffusion Models, Vision-Language Models, zero-shot detection).

• Experience deploying models to Edge Devices (NVIDIA Jetson, Raspberry Pi, Android/iOS with TFLite/CoreML).

• Track record of published research papers (IEEE, CVPR, ICCV, ECCV) or top-tier Kaggle computer vision achievements.

Success Metrics

• Production Deployment: Timely delivery of robust, high-accuracy CV models into production pipelines.

• Business Value Alignment: Measurable positive ROI or operational enhancement resulting from delivered AI features.

• Technical Quality: Maintaining clean, reproducible codebases and automated pipelines with minimal inference latency and high model reliability.

• Team Knowledge Growth: Effective cross-collaboration with full-stack and cloud teams, fostering rapid capability growth across the engineering group.

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.

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