Bosch Group: Research Engineer – Generative AI and Computer Vision — bengaluru, India

About Bosch Group

Bosch's India activities span mobility, industrial technology, consumer goods, and energy and building technology. Its employer description identifies engineering development, manufacturing and commercial operations within that range. The group operates through multiple Indian businesses, including activities in automotive systems, software and engineering, industrial technology and home appliances. This makes Bosch a collection of related technology businesses rather than a single product operation, with different entities serving different industrial and consumer applications.

The position and its setting

Bosch Group lists Research Engineer – Generative AI and Computer Vision in bengaluru, India, under reference REF295548S. The details below separate the work described by the employer from essential conditions, preferred experience and practical preparation advice.

• Employment type: Full-time
• Employer listing date: 15 October 2026
• Contract type: Unlimited
• Legal Entity: Bosch Global Software Technologies Private Limited
• Brands: Bosch Group

Responsibilities and work described

• The Research Engineer – Generative AI and Computer Vision focuses on advancing state-of-the-art Generative AI and deep learning methods for real-world computer vision applications
• The role combines applied research, experimentation, and engineering to develop robust AI solutions for image understanding, synthetic data generation, visual inspection, and continuous model monitoring
• The role will contribute to applied research and development of scalable AI capabilities spanning the full computer vision lifecycle—from data preparation and model development to deployment, monitoring, and continuous improvement
• The work will address practical challenges such as limited or imbalanced training data, rare events, changing image conditions, data drift, and model-performance degradation in operation
• Generative AI and Synthetic Data: Research, design, and implement Generative AI approaches for creating realistic and diverse synthetic image datasets
• Explore diffusion models, generative adversarial networks, image-to-image generation, controllable generation, and related techniques to address limited data, rare defects, class imbalance, and long-tail inspection scenarios
• Computer Vision Model Development: Develop and evaluate deep learning models for industrial image inspection tasks such as image classification, defect detection, anomaly detection, object detection, segmentation, and representation learning
• Work: with CNN-based, transformer-based, and multimodal architectures
• Data-Efficient Learning: Investigate methods including transfer learning, self-supervised learning, few-shot learning, domain adaptation, active learning, and synthetic data augmentation to reduce annotation effort and improve model performance under limited-data conditions
• Model Monitoring and Robustness: Develop methods for continuously monitoring deployed inspection models and image-data pipelines
• This includes detecting data drift, changes in image conditions, model-performance degradation, confidence shifts, unusual defect patterns, and out-of-distribution inputs
• Research and Experimentation: Formulate research hypotheses, design controlled experiments and ablation studies, establish appropriate evaluation benchmarks, and analyze results with scientific rigor
• Compare state-of-the-art methods and identify approaches suitable for industrial deployment
• Prototype and Product Integration: Build proof-of-concept solutions and research prototypes using Python and modern deep learning frameworks
• Collaboration: with software, cloud, MLOps, manufacturing, and domain teams to integrate validated methods into scalable AI platforms and applications
• Scalable AI Lifecycle: Contribute to capabilities covering data preparation, model training, validation, deployment, monitoring, retraining, and continuous improvement across the complete computer vision model lifecycle

Essential requirements and candidate background

• Ph.D. or M.S. or M
• Tech from top Indian or foreign institutes (IITs, IIITs, IISc etc.) in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Electrical Engineering, Electronics and Communication Engineering, Robotics, Mechatronics, Applied Mathematics, or a closely related field
• Experience:
• At least 3 years of relevant professional or applied research experience in computer vision, deep learning, Generative AI, machine learning, or related areas
• Mandatory/requires Skills:
• Understanding: machine learning, deep learning, and computer vision fundamentals
• Hands-on experience developing computer vision solutions using techniques such as convolutional neural networks (CNNs) and other sequence models where applicable, transformers, autoencoders, and other modern neural-network architectures
• Proficiency: Python and hands-on experience with PyTorch
• Experience: supporting libraries such as NumPy, pandas, OpenCV, scikit-learn, and common image-processing libraries
• Practical experience with transformer-based vision models, such as Vision Transformers, hierarchical vision transformers, detection transformers, or related architectures
• Hands-on knowledge of Generative AI methods for image data, particularly diffusion models, generative adversarial networks, variational autoencoders, or image-to-image generation methods
• Experience designing synthetic data-generation or advanced data-augmentation pipelines for training and evaluating computer vision models
• Experience: one or more computer vision tasks such as image classification, object detection, semantic or instance segmentation, anomaly detection, defect detection, or visual similarity learning
• Experience: the Hugging Face ecosystem (models, libraries, and SDKs) for rapid prototyping and evaluation of state-of-the-art AI models
• Experience: model evaluation methodologies and metrics (such as Precision, Recall, F1-score, AUC, and ROC), including the rigorous interpretation of results to optimize model decision thresholds
• Formulate research questions, design rigorous experiments and ablation studies, establish evaluation metrics, benchmark alternative approaches, and interpret empirical results
• Understanding: model generalization, domain shift, data drift, out-of-distribution detection, uncertainty estimation, and model-performance monitoring
• Good software-engineering practices, including version control, modular implementation, code reviews, testing, technical documentation, and reproducible experimentation
• Analytical, problem-solving, presentation, and communication skills
• Collaborate effectively with interdisciplinary and geographically distributed teams

Preferred experience and additional strengths

• Experience applying computer vision to industrial quality inspection, automated optical inspection, manufacturing, robotics, medical imaging, autonomous systems, or other safety- or quality-critical applications
• Experience: synthetic defect generation, rare-event simulation, controllable image generation, domain randomization, or simulation-to-real transfer
• Familiarity: vision-language models, multimodal foundation models, zero-shot or open-vocabulary vision methods, and parameter-efficient model adaptation (PEFT techniques such as LoRA, QLoRA etc.)
• Knowledge: self-supervised learning, contrastive learning, active learning, weakly supervised learning, few-shot learning, and continual learning
• Experience: monitoring metrics and techniques such as input-data drift, feature drift, prediction drift, confidence calibration, outlier detection, defect-rate analysis, and alert generation
• Familiarity: MLOps practices and tools for experiment tracking, model registries, automated training pipelines, containerization, deployment, observability, and model lifecycle management
• Experience deploying machine learning models using cloud platforms and container technologies such as Docker and Kubernetes
• Knowledge: model optimization methods—including quantization, pruning, knowledge distillation, efficient inference, and deployment on edge or resource-constrained systems—with exposure to tools like TensorRT and ONNX Runtime, and industrial vision standards/software like NeuroCheck and MVTec HALCON
• Publications in recognized AI, machine learning, or computer vision venues, or demonstrated experience contributing to patents and invention disclosures
• Experience working with large-scale real-world image datasets and developing AI solutions beyond academic benchmark datasets
• Familiarity: GenAI-assisted software-development and research tools to improve experimentation and development productivity

Location, schedule and contract conditions

• Work arrangement: On-site
• Advertised location: bengaluru, India

Conditions requiring the complete notice

Some passages contain complex or combined conditions that have not been reduced to short eligibility statements here. Read the complete linked notice for those passages, including any alternatives, exceptions or restrictions. The concise facts above should not be treated as the full wording of every condition.

Pay and application dates

A verified salary figure is not established in this article. An online estimate for another Research Engineer – Generative AI and Computer Vision vacancy is not an offer for reference REF295548S; use the employer’s own pay disclosure and recruitment communication.

A closing date is not confirmed in this article. Check the current employer page before submitting: an undated posting can change or be withdrawn.

How to apply to Bosch Group

Applications follow the official employer page linked below.

Open the official Bosch Group posting below and match the title and reference REF295548S.

Compare your qualifications and experience with the essential conditions for the bengaluru, India position, then read the preferred criteria separately.

Prepare an accurate CV and the supporting items requested in that employer’s application. Provide only information and documents you are authorised to share.

Use the employer’s application option, complete the required fields and review the submission. Retain the acknowledgement when one is provided. Job Alerts World does not receive the application.

Practical preparation: Python

Python work is easier to assess through a concrete example than a list of libraries. Explain the problem, the input data, the parts you implemented and how you checked the output. Include a repository or a short technical note when you can share it.

Relevant skills

• Python
• Kubernetes
• Docker
• AI/ML
• AI
• manufacturing
• training
• stakeholder management
• machine learning
• electrical engineering