Job Description

1-year contract, renewable
Hybrid work arrangement
Government project

We are seeking a Systems Engineer with strong foundations in cloud infrastructure, DevOps, and system integration, complemented by specialized expertise in AI/ML technologies and video analytics applications.

As you will be working on a systems engineering role with AI/ML serving as augmented skillsets, core systems engineering competencies such as cloud platforms, DevOps practices, API integration are mandatory foundations.

Operating at the intersection of systems engineering, edge computing, and AI/ML technologies, this role drives the design, deployment, and operation of intelligent transportation systems for Singapore's Land Transport Authority, ensuring AI-powered solutions are architected with systems engineering rigor and operated with production reliability.

Key Responsibilities

Design and build application/cloud/edge infrastructure for AI/ML experimentation and prototyping with established frameworks and best practices
Set up CI/CD pipelines for ML model testing, training, and deployment automation
Deploy and evaluate AI/ML models on cloud and edge platforms with focus on system integration and production readiness
Translate stakeholder needs into actionable system requirements with full traceability
Integrate emerging AI technologies with existing LTA infrastructure ensuring seamless interoperability
Conduct proof-of-concept implementations focusing on deployment feasibility and performance benchmarks
Automate data pipelines and model training workflows using DevOps and MLOps practices
Foster cross-functional collaboration among AI/ML engineers, developers, security teams, and operations staff
Deploy and operate production-ready video analytics systems at scale across LTA infrastructure
Build and manage edge computing infrastructure with containerization, monitoring, and automated health checks
Implement comprehensive monitoring, logging, and alerting for AI/ML production systems
Integrate video analytics solutions with LTA's APIs, databases, message queues, and network systems
Optimize system performance, GPU allocation, and real-time processing for latency and throughput requirements
Work with ML platforms to support model development, versioning, and deployment pipelines
Implement security, compliance, and data governance including PDPA compliance, encryption, and access controls
Maintain and troubleshoot deployed infrastructure with systematic debugging and root cause analysis
Monitor system health, implement automated remediation, and respond to production incidents
Manage model retraining pipelines and version control ensuring reproducibility and rollback capabilities
Provide technical support for infrastructure, networking, and AI/ML system performance issues
Collaborate with operations teams ensuring reliability, uptime SLAs, and operational excellence

Qualifications

Bachelor's degree in Computer Science, Engineering, Information Systems, or related technical field (required)
Master's degree in AI/ML, Computer Vision, Computer Science, or related field (preferred)
AWS certifications: Solutions Architect, Machine Learning Specialty, DevOps Engineer Professional
Kubernetes certifications: CKA, CKAD
NVIDIA Deep Learning Institute certifications
Cloud Platforms: AWS infrastructure and services including EC2, S3, Lambda, VPC, IAM, CloudWatch, ECS/EKS; cloud architecture for compute, storage, networking, and security
DevOps & Automation: Git version control, CI/CD pipelines, Infrastructure as Code, Docker containerization, Kubernetes orchestration, automated testing and deployment strategies
Systems Integration: RESTful API design and development, data pipelines and ETL processes, database integration, networking fundamentals, distributed systems and microservices
Programming: Python/TypeScript/Java/similar for systems automation and ML applications; scripting for infrastructure management; production-grade code practices including testing, logging, and documentation
AI/ML Frameworks: PyTorch, TensorFlow for computer vision; OpenCV for image/video processing; object detection and tracking algorithms; model optimization techniques
Edge AI & IoT: Nvidia Jetson platform development and optimization; edge computing deployment strategies; IoT camera infrastructure; real-time data processing
ML Operations: AWS SageMaker and ML services; ML platforms experience; model training, versioning, and deployment pipelines; production model monitoring and maintenance
Traffic monitoring and analysis: vehicle detection, traffic flow, congestion analysis
Safety and compliance monitoring: violation detection, construction safety, pedestrian safety
Experience with transportation, smart city projects, or intelligent transportation systems is a strong plus
Strong systems-thinking mindset to decompose complex problems into modular solutions
Excellent communication and stakeholder management translating technical concepts to non-technical audiences
Ability to balance technical depth with production delivery and operational reliability
Proactive, collaborative, and adaptable in fast-evolving technical environments
Documentation expertise: technical documentation, architecture diagrams, runbooks, API documentation
Mentoring and knowledge transfer capabilities
Agile/project management experience

Experience Requirements

Minimum 3 years hands-on experience as Systems Engineer working with cloud infrastructure, DevOps practices, and system integration in production environments
Proven track record deploying and operating production systems at scale with reliability, monitoring, and incident response
Strong foundation in system architecture, networking, security, and distributed systems design
Demonstrated experience applying AI/ML technologies in production including model deployment and lifecycle management
Experience with edge AI deployment on Nvidia Jetson or similar platforms highly valued
Experience with video analytics, computer vision, or outdoor AI applications is a plus
Government or public sector project experience with compliance requirements advantageous
Portfolio demonstrating successful AI/ML deployment integrated with enterprise/government infrastructure
Ability to work with non-technical stakeholders and translate complex technical concepts clearly
Understanding of data privacy, security, and compliance in government contexts
Problem-solving mindset with ability to work independently and in cross-functional teams
On-call availability for production infrastructure support on rotation basis
Ability to respond to incidents with systematic troubleshooting under pressure