DL
Azure Machine Learning _Bangalore
Diverse Lynx India
🇮🇳 India
On-site
1 week ago
- Azure
- Machine Learning
- AI/ML
- AI
- TensorFlow
- PyTorch
- ONNX
- Raspberry Pi
- Python
- REST API
- Docker
- Kubernetes
- CI/CD
- Computer Vision
- OpenCV
- MLOps
- MLflow
- IoT
1 week ago
SNRequired InformationDetails1RoleSr. AI ML Developer2Required Technical Skill SetAI ML| Edge AI3No of Requirements14Desired Experience Range8+ yrs5Location of Requirement HyderabadDesired Competencies (Technical/Behavioral Competency)Must-Have1.5+ years of hands-on development experience in AI/ML.2.Strong knowledge of ML libraries (TensorFlow Lite| PyTorch Mobile| ONNX).3.Experience with edge hardware platforms (e.g.| Raspberry Pi| Nvidia Jetson| etc..).4.Proficient in Python and C/C++.5.Familiarity with performance optimization techniques for models on edge.6.Experience with REST APIs| messaging protocols| or low-latency data streaming.7.Ability to perform predictive and statistical analysis from different data source8.knowledge and hands-on experience of building and deploying AI models on edge devices.9.knowledge of embedded systems| microcontrollers| or low-power compute devices.10.Experience with containerization (Docker)| orchestration (Kubernetes)| and CI/CD pipelines11.Experience with Image Processing| Computer Vision| Pattern Recognition| Machine Learning and Linear algebra.12.knowledge and exposure to model optimization techniques.13.Experience with AI accelerator frameworksGood-to-Have1.Familiarity with OpenCV| YOLO| or MobileNet for vision tasks.2.Knowledge of TinyML or microcontroller-based AI inference.3.Exposure to MLOps tools and versioning (MLflow| DVC).4.Understanding of security practices in edge deployments.5.Experience with edge analytics| anomaly detection| or predictive maintenance use cases.6.Exposure to deployment tool-chain like Client EII| Nvidia Deep Stream| Qualcomm AI Hub| etc....7.Excellent communication and documentation skills8.Exposure to popular platforms such as Azure| AWS.SNResponsibility of / Expectations from the Role 11.Build and optimize AI/ML models for edge deployment.2.Develop edge inference pipelines using lightweight frameworks.3.Optimize models for resource-constrained environments (quantization| pruning).4.Integrate AI models into embedded or IoT platforms.5.Collaborate with cross-functional teams on data collection| preprocessing| and annotation.6.Implement software for real-time processing and decision-making at the edge.
Azure Machine Learning _Bangalore · Diverse Lynx India