Deep Learning Engineer
Full Job Description
GENERAL ELECTRIC (GE) in Bengaluru is seeking a Staff Deep Learning Engineer specializing in Computer Vision. This role is pivotal in designing, developing, and operationalizing advanced computer vision models for large-scale remote visual inspection (RVI) systems. The ideal candidate will possess the ability to work independently, define technical direction, and build state-of-the-art deep learning pipelines from inception. Responsibilities span the full lifecycle of deep learning systems, including data preparation, model design, training, optimization, deployment, and performance monitoring, in close collaboration with MLOps engineers, software teams, and product groups. As a Staff engineer, you will serve as a technical authority, mentor junior engineers, and promote engineering excellence. RVI systems are crucial for high-precision, non-contact inspection of industrial components using advanced imaging and AI-driven analytics. This position offers the opportunity to shape the future of intelligent inspection by developing deep learning models for real-time detection, measurement, and classification in challenging, high-speed industrial environments. You will build and optimize algorithms for large-scale inference pipelines, integrating seamlessly with cutting-edge camera and sensor systems.
Key Responsibilities:
Deep Learning Computer Vision (Staff Level)
- Define and own the overall deep learning architecture for computer vision systems across the organization.
- Design, train, and optimize models for detection, segmentation, classification, and tracking.
- Lead technical decisions on model architectures, training strategies, evaluation methodologies, and optimization techniques.
- Ensure scalable, efficient, and robust inference for cloud, edge, and embedded deployments.
- Establish best practices for model development, data preparation, experimentation, and model lifecycle management.
Data Engineering for Deep Learning
- Define data requirements and collaborate with data teams to build high-quality training and evaluation datasets.
- Develop dataset preparation, augmentation, curation, and validation pipelines.
- Contribute to the evolution of the computer vision data lake and related data infrastructure.
Model Deployment Integration
- Collaborate closely with MLOps teams to integrate deep learning models into production-grade pipelines.
- Optimize models for real-time, large-scale inference, including quantization, pruning, and hardware-specific acceleration.
- Support deployment across cloud platforms (AWS, GCP, Azure) and edge environments.
Cross Team Collaboration & Leadership
- Mentor junior engineers and guide the team as the technical authority for deep learning and computer vision.
- Work closely with algorithm developers, MLOps engineers, software engineers, and product teams.
- Champion software excellence, model reliability, and performance across the organization.
- Stay current on deep learning research, emerging architectures, and industry best practices.
Requirements:
Core Technical Skills
- Bachelor's/Master's degree in Computer Science, Engineering, or related field.
- 7+ years of experience in Deep Learning, Computer Vision, and Python (staff-level contribution expected).
- Strong understanding of ML fundamentals, optimization, and model lifecycle management.
- Advanced Python skills; proficiency in C++ is a strong plus.
Deep Learning Computer Vision Expertise
- Hands-on experience with PyTorch, TensorFlow, and modern CV frameworks.
- Strong experience building and deploying production-grade CV systems.
- Expertise in object detection, semantic/instance segmentation, and tracking models (e.g., YOLO, SSD, Faster R CNN, transformers).
- Experience with model optimization for latency, throughput, and memory efficiency.
MLOps & Data Collaboration Focus
- Familiarity with MLflow, Kubeflow, TFX, or similar platforms for model tracking and lifecycle management.
- Understanding of dataset management systems, scalable data pipelines, and feature storage.
- Experience collaborating with MLOps teams on model deployment and monitoring.
Cloud Deployment & Infrastructure
- Experience deploying ML models on Azure ML, AWS SageMaker, or GCP Vertex AI.
- Strong understanding of Docker, Kubernetes, and GitLab CI/GitHub Actions.
- Understanding of hardware acceleration (GPU, NPU, tensor cores) and edge inference constraints.
Soft Skills
- Demonstrated technical leadership on complex AI systems.
- Excellent problem-solving, communication, and collaboration skills.
- Ability to operate independently, influence architecture decisions, and drive innovation.
Company
GENERAL ELECTRIC (GE)
GENERAL ELECTRIC (GE) is a global leader in industrial technology. While specific company description details were not provided, GE is known for its innovation across various sectors including aviatio...