Principal Engineer, Machine Learning Full-time

Kinetics Training Solutions (KTS) is a business unit of ST Engineering Land Systems Ltd. KTS delivers training solutions to both Military and Commercial sectors. KTS training covers driver training qualification, vehicle technician training, specialized armament trainings and training development consultancy. As a Machine Learning Engineer in KTS, you should possess a keen interest in Artificial Intelligence (AI) Data Analytics. Candidates should demonstrate resourcefulness, possess analytical traits and systematic approach to problem solving. He/she is meticulous in details and quality of work.

Responsibilities:

In this role, you will be involved with the end-to-end implementation of deep learning models to tackle a broad range of real-world video analytics problems. You will be expected to translate state-of-the-art techniques into working products and deploy them at scale.

Our product development team has demonstrated its ability to create innovative solutions in the area of video analytics, and we are poised for the next phase of growth and expansion.

• Develop production quality video analytics software
• Carry out research to develop new techniques to improve performance of existing
• Evaluate deep learning models
• Develop tools that help accelerate machine learning workflows
• Develop technical proposal and presentations to potential clients

Requirements:

• Experience with training models on deep learning frameworks like TensorFlow, Keras, PyTorch, Caffe2 and/or MXNet for computer vision tasks such as object detection, classification, and tracking
• Experience using MLOps platforms such as MLFlow, KubeFlow, AWS Sagemaker, Azure ML, etc
• Proficient in mainstream programming languages, preferably C/C++, Python, JavaScript, PHP, etc
• Proficient in Window, added advantage with OS for mobile (iOS, Android) and OS for embedded system would be advantageous.
• Knowledge specific libraries for image processing, encryption, OpenCV, OpenSSL, PCL etc. would be preferred.
• Experienced the full life cycle of model training to deployment into production
• Familiarity with working in a Linux environment on public clouds and locally
• Understanding of deep learning optimization concepts such as pruning, quantization, and the impact of accelerators on throughput, latency and cost
• An explorative personality with a hunger for continuous improvements
• Strong problem solving, analytical and conceptual skills, with good communication skills
• Able to work independently with minimum guidance and monitoring, a good team player