G01 - Systems Engineer

We are looking for a Systems Engineer with strong expertise in cloud infrastructure, DevOps, and system integration, complemented by exposure to AI/ML and video analytics. This role focuses on designing, deploying, and operating intelligent transportation systems combining systems engineering rigor with AI-driven solutions. Responsibilities Design and build cloud, application, and edge infrastructure for AI/ML solutions Develop CI/CD pipelines for model training, testing, and deployment Deploy AI/ML models across cloud and edge environments Translate business requirements into system-level designs Integrate AI solutions with existing infrastructure and systems Build proof-of-concepts to validate feasibility and performance Automate data pipelines and ML workflows using MLOps practices Collaborate with cross-functional teams across engineering, security, and operations Deploy and manage large-scale video analytics systems Manage edge infrastructure with containerization and monitoring Implement logging, monitoring, and alerting systems Integrate solutions with APIs, databases, and messaging systems Optimize system performance including latency, throughput, and GPU utilization Support ML lifecycle including deployment, versioning, and monitoring Ensure security, compliance, and data governance standards Troubleshoot and maintain infrastructure and deployed systems Monitor system health and respond to incidents Manage model retraining pipelines and version control Ensure system reliability and SLA adherence Provide technical support across infrastructure and AI/ML systems Requirements Bachelor's degree in Computer Science, Engineering, or related field Master's degree in AI/ML or related disciplines is preferred Minimum 3 years of experience in Systems Engineering across cloud, DevOps, and system integration Experience deploying and operating production systems at scale Strong foundation in system architecture, networking, and security Hands-on experience in deploying AI/ML solutions in production Experience with edge AI platforms such as Nvidia Jetson is a plus Exposure to video analytics or computer vision is advantageous Technical Skills Cloud platforms such as AWS (EC2, S3, Lambda, VPC, IAM, ECS/EKS) DevOps tools and practices including CI/CD, Git, Docker, Kubernetes, and Infrastructure as Code System integration including REST APIs, ETL pipelines, databases, and distributed systems Programming languages such as Python, Java, or TypeScript Experience with AI/ML frameworks such as PyTorch, TensorFlow, and OpenCV is good to have Familiarity with edge AI, IoT deployment, and MLOps tools such as SageMaker is advantageous Originally posted on Himalayas