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