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 suc