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.ResponsibilitiesDesign and build cloud, application, and edge infrastructure for AI/ML solutionsDevelop CI/CD pipelines for model training, testing, and deploymentDeploy AI/ML models across cloud and edge environmentsTranslate business requirements into system-level designsIntegrate AI solutions with existing infrastructure and systemsBuild proof-of-concepts to validate feasibility and performanceAutomate data pipelines and ML workflows using MLOps practicesCollaborate with cross-functional teams across engineering, security, and operationsDeploy and manage large-scale video analytics systemsManage edge infrastructure with containerization and monitoringImplement logging, monitoring, and alerting systemsIntegrate solutions with APIs, databases, and messaging systemsOptimize system performance including latency, throughput, and GPU utilizationSupport ML lifecycle including deployment, versioning, and monitoringEnsure security, compliance, and data governance standardsTroubleshoot and maintain infrastructure and deployed systemsMonitor system health and respond to incidentsManage model retraining pipelines and version controlEnsure system reliability and SLA adherenceProvide technical support across infrastructure and AI/ML systemsRequirementsBachelor's degree in Computer Science, Engineering, or related fieldMaster's degree in AI/ML or related disciplines is preferredMinimum 3 years of experience in Systems Engineering across cloud, DevOps, and system integrationExperience deploying and operating production systems at scaleStrong foundation in system architecture, networking, and securityHands-on experience in deploying AI/ML solutions in productionExperience with edge AI platforms such as Nvidia Jetson is a plusExposure to video analytics or computer vision is advantageousTechnical SkillsCloud platforms such as AWS (EC2, S3, Lambda, VPC, IAM, ECS/EKS)DevOps tools and practices including CI/CD, Git, Docker, Kubernetes, and Infrastructure as CodeSystem integration including REST APIs, ETL pipelines, databases, and distributed systemsProgramming languages such as Python, Java, or TypeScriptExperience with AI/ML frameworks such as PyTorch, TensorFlow, and OpenCV is good to haveFamiliarity with edge AI, IoT deployment, and MLOps tools such as SageMaker is advantageousOriginally posted on Himalayas