Responsibilities

• Lead the full lifecycle of computer vision and machine learning model development — from data collection and labeling through deployment and monitoring in production environments
• Lead research and implementation of deep learning models for vision tasks such as detection, segmentation, classification, tracking, and real-time inference
• Help in planning and executing AI/ML project phases in alignment with product and platform goals, from prototyping to scalable production rollouts
• Work closely with product, hardware, and cloud teams to design end-to-end intelligent features integrated into devices and applications
• Help in designing and building scalable pipelines for training, evaluating, and deploying models on both cloud and edge platforms
• Develop logical solutions to complex physical-world problems using structured experimentation and performance tuning
• Team with other senior engineers and cross-functional partners to architect technically sound, performant, and reliable AI systems
• Prioritize and manage multiple initiatives, ensuring that all models and systems meet quality, performance, and compliance standards
• Stay current with advancements in Computer Vision ML and AI, particularly in foundational vision models, and bring those innovations into the product roadmap
• Understand the real-world implications of emerging AI/ML trends and apply them to support business transformation through intelligent automation

Skills And Expertise

• 7+ years of experience in machine learning, with a strong focus on computer vision and applied deep learning
• Proven track record of delivering AI/ML solutions from research to production in fast-paced, real-world environments
• Experience with the full ML development lifecycle — data processing, model training, evaluation, optimization, and deployment
• Deep hands-on technical skills with frameworks such as PyTorch or TensorFlow, and programming expertise in Python (C++ is a plus)
• Deep expertise in convolutional and transformer-based vision models, model compression, and inference optimization
• Experience deploying models in cloud environments (AWS/GCP/Azure) as well as to edge devices (using TensorRT, ONNX, or TFLite)
• Excellent team work and collaboration skills; ability to work cross-functionally across software, hardware, and product teams
• Strong problem-solving skills and the ability to translate ambiguous challenges into measurable solutions
• Ability to communicate complex technical concepts clearly and effectively to both technical and non-technical stakeholders
• Self-driven, proactive, and thrives in a dynamic, startup-like environment

Bonus Criteria

• Experience applying AI/ML based CV solutions to real-world IoT systems or edge intelligence platforms (e.g., surveillance, monitoring, and anomaly detection)
• Proficiency in Linux/Ubuntu environments; comfort with scripting and tooling around data and deployment workflows
• Familiarity with Agile methodologies and collaborative development tools (JIRA, Git, etc.)
• Background with embedded systems, containers (Docker), or Linux-based deployment pipelines
• Prior experience in startup or high-growth environments, especially building 0-to-1 AI solutions