Job Overview

Airbus is hiring an experienced Data Scientist specializing in Computer Vision for its Bangalore Area team. This is a hands-on role focused on taking vision-based artificial intelligence solutions from an initial business problem through data preparation, model development, optimization, deployment, monitoring, and measurement of business value.

The position combines advanced machine learning with practical software and cloud engineering. You will work on computer vision problems such as image classification, object detection, segmentation, tracking, OCR, and visual inspection. The role also covers dataset creation, annotation strategy, scalable ML pipelines, model optimization, MLOps, and deployment to cloud or edge environments.

A major part of the role is connecting technical outcomes with real business needs. You will work with business leaders and product teams to define useful computer vision solutions, establish meaningful KPIs, and evaluate how AI systems improve operational performance. You will also guide ML and full-stack engineers and contribute to engineering standards and experimentation practices.

Key Responsibilities

• Own the computer vision model lifecycle from problem definition and data preparation through production deployment and monitoring.
• Design, train, evaluate, and optimize models for classification, object detection, segmentation, tracking, OCR, and visual inspection.
• Establish reliable data collection, cleaning, labeling, and dataset curation processes.
• Define annotation strategies and quality guidelines for computer vision training data.
• Evaluate and use annotation platforms such as CVAT and Labelbox when appropriate.
• Work directly with business and product stakeholders to convert unclear requirements into well-defined computer vision problems.
• Establish technical KPIs and connect model performance with measurable business outcomes.
• Design scalable machine learning pipelines on AWS or GCP.
• Use containers and managed or serverless cloud services to support production ML workloads.
• Optimize models for efficient inference through techniques such as quantization, compression, and runtime optimization.
• Apply technologies such as ONNX, TensorRT, or OpenVINO where appropriate for performance-sensitive deployments.
• Establish monitoring for model performance, reliability, and drift.
• Guide ML and full-stack engineers on model design, coding practices, experimentation, and research methods.
• Evaluate new computer vision, Vision-Language Model, and AI architectures.
• Assess build-versus-buy options and introduce practical innovations when they provide business value.
• Maintain reproducible and scalable development and deployment processes.

Required Skills

Strong Python skills and deep practical knowledge of machine learning are essential. Candidates should have advanced experience with PyTorch or TensorFlow/Keras and a strong understanding of how deep learning models are developed and evaluated for production use.

The role requires substantial computer vision expertise, including practical knowledge of camera fundamentals, image and video encoding, camera calibration, 3D reconstruction, and multi-camera multi-object tracking. Experience with OpenCV and other computer vision frameworks is highly relevant.

Candidates should understand production ML engineering rather than model development in isolation. Experience with Git, unit testing, modular software design, Docker, REST APIs, and experiment tracking is important. Knowledge of FastAPI or Flask can support the API development requirements.

Experience with cloud-based machine learning is required on AWS or GCP. Candidates should be comfortable designing scalable ML solutions and working with cloud storage, compute, deployment, and managed AI services.

Preferred Skills

Experience with tools such as CVAT, Labelbox, DVC, MLflow, and Weights & Biases is valuable. Familiarity with NVIDIA DeepStream and COLMAP can also strengthen a candidate's profile.

Experience optimizing models with ONNX, TensorRT, or OpenVINO is useful, particularly for low-latency inference. Candidates with exposure to MLOps, model monitoring, dataset versioning, relational or NoSQL databases, and production automation should highlight those capabilities.

Knowledge of Vision-Language Models, diffusion models, zero-shot detection, and other modern AI approaches is an advantage. Experience deploying computer vision models to edge devices such as NVIDIA Jetson, Raspberry Pi, Android, or iOS can also be beneficial.

Professional AWS or GCP machine learning certifications are listed as additional advantages. Research publications in areas such as computer vision or strong computer vision results in Kaggle competitions may also strengthen an application.

Education

A Bachelor's or Master's degree in Computer Science, Data Science, Electrical

Engineering, Mathematics, or a related quantitative discipline is required.

Experience

The position requires at least 5 years of hands-on experience in Data Science and Machine Learning, including at least 3 years focused specifically on production Computer Vision applications. Candidates should be able to demonstrate ownership of real-world ML solutions rather than only academic or experimental projects.

Relevant experience may include production model development, computer vision pipelines, dataset management, cloud deployment, MLOps, model optimization, or AI solution architecture.

Required Technologies

Core technologies and technical areas include Python, PyTorch, TensorFlow, Keras, OpenCV, NVIDIA DeepStream, COLMAP, AWS, GCP, Docker, Git, REST APIs, FastAPI, Flask, MLflow, Weights & Biases, DVC, ONNX, TensorRT, OpenVINO, and relational or NoSQL databases.

AWS technologies mentioned include SageMaker, S3, EC2, Lambda, Rekognition, ECR, and EKS. GCP technologies include Vertex AI, Google Cloud Storage, Cloud Run, Vision API, and Compute Engine.

The role also covers computer vision and AI techniques including object detection, object localization, feature extraction, feature matching, camera calibration, 3D reconstruction, multi-camera tracking, OCR, segmentation, classification, Vision-Language Models, diffusion models, and zero-shot detection.

Soft Skills

Strong analytical thinking is important for solving difficult visual-data problems and understanding edge cases. Candidates should be able to approach technical challenges systematically and make practical decisions based on evidence.

Clear communication is equally important. The role requires presenting technical findings, model performance, trade-offs, and recommendations to both technical teams and business leaders.

Business awareness is valuable because the success of a computer vision solution is measured not only by technical metrics such as mAP, IoU, and F1-score, but also by its impact on operational efficiency, cost, accuracy, and other business goals.

Benefits of Working in this Role

This role offers the opportunity to work on production-grade Computer Vision and AI solutions while combining data science, cloud engineering, MLOps, and software development. Engineers can gain broad exposure to modern vision architectures, scalable ML infrastructure, model optimization, and cloud deployment.

The position also provides opportunities to collaborate with business stakeholders and mentor other engineers, helping develop both technical depth and solution leadership skills.

Work Mode

The provided job description does not specify a remote or hybrid work arrangement. The position is listed as full time and based in the Bangalore Area.

Location

Bangalore Area, Karnataka, India.

Who Should Apply

This opportunity is suited to experienced Data Scientists and Machine Learning Engineers with strong production Computer Vision experience. Candidates should have at least 5 years of relevant Data Science or ML experience and at least 3 years working specifically on Computer Vision applications in production.

Applicants should emphasize practical work involving Python, PyTorch or TensorFlow, computer vision models, cloud ML platforms, data pipelines, model deployment, and MLOps. Experience connecting technical metrics to business outcomes is especially relevant.

Candidates with experience in AWS or GCP, OpenCV, Docker, FastAPI, MLflow, DVC, ONNX, TensorRT, OpenVINO, Vision-Language Models, or edge deployment should clearly describe their project contributions.

Career Growth

Experience across Computer Vision, Machine Learning, cloud architecture, MLOps, and production AI can support future careers such as Senior Data Scientist, Computer Vision Engineer, Machine Learning Engineer, AI Solutions Architect, MLOps Engineer, Applied Scientist, or AI Technical Lead.

The leadership and stakeholder responsibilities can also help experienced engineers move toward technical ownership, AI solution leadership, and mentoring responsibilities.

Application Advice

Build your resume around production Computer Vision outcomes rather than listing tools alone. Explain the business problem, dataset strategy, model approach, evaluation method, deployment environment, and measurable result for important projects.

Highlight experience with classification, detection, segmentation, tracking, OCR, visual inspection, or other relevant vision applications. Include details about model optimization, inference latency, monitoring, model drift, and reliability where applicable.

Clearly mention your cloud experience with AWS or GCP and list the specific services you have used. Include practical examples of MLOps, CI/CD, Docker, API development, dataset versioning, and experiment tracking.

If you have worked with Vision-Language Models, diffusion models, edge devices, research publications, or computer vision competitions, include those details as supporting evidence of your technical depth.

Apply now through SoftoJobs to explore this opportunity.