AI Platform Engineer
Senior Level Position
Position Overview
Join our elite engineering team to build and maintain the core AI infrastructure that powers Multisector Hubs’ revolutionary corporate governance solutions. You’ll architect scalable platforms supporting 10,000+ corporate entities while driving innovation in MLOps and real-time AI decision systems.
🏗️ Infrastructure Design
Design and implement AI infrastructure supporting 10,000+ corporate entities with enterprise-grade reliability.
🔄 MLOps Pipelines
Build continuous model deployment and monitoring systems for seamless AI operations.
⚡ Performance Optimization
Optimize AI model performance for real-time corporate governance decisions.
🤝 Cross-team Collaboration
Partner with AI/ML engineers to productionize research models at scale.
📋 Requirements
5+ years experience in platform engineering or DevOps
Degrees in Computer Science, Engineering, or related field.
⚙️ Key Responsibilities
Infrastructure Architecture & Design
Lead the design and implementation of scalable AI infrastructure that supports enterprise-level corporate governance solutions.
• Design cloud-native AI platforms with high availability and fault tolerance.
• Architect data pipelines for real-time and batch processing
• Implement security best practices for AI systems handling sensitive corporate data.
Performance Optimization
Optimize AI model performance to meet real-time requirements, system performance, latency, and resource utilization for corporate governance & AI decision-making.
• Implement model serving optimizations for low-latency inference.
• Design auto-scaling solutions for variable workloads.
• Optimize resource utilization and cost efficiency.
MLOps Pipeline Development
Build and maintain comprehensive MLOps pipelines for continuous integration, deployment, and monitoring of AI models.
• Develop automated model training and validation workflows.
• Implement A/B testing frameworks for model performance evaluation.
• Create monitoring dashboards for model drift and performance metrics.
Cross-functional Collaboration
Work closely with AI/ML engineers, data scientists, and product teams to productionize research models.
• Bridge the gap between research and production environments.
• Provide technical guidance on platform capabilities and limitations.
• Mentor junior engineers and contribute to technical documentation.
Technical Leadership
Mentor engineering teams, establish best practices, and drive technical excellence across the organization.
🎯 Hiring Process
Duration: 30 minutes
Duration: 60 minutes
Duration: 45 minutes
🌟 Onboarding Journey
Complete security clearance, access setup, meet your team, and dive into our platform architecture documentation.
Shadow senior engineers, understand our MLOps workflows, and begin contributing to ongoing infrastructure projects.
Take ownership of key platform components, lead technical discussions, and begin mentoring junior engineers.
🌟 Ongoing Development & Support
📚 Continuous Learning
• $3,000 annual learning & development budget
• Conference attendance and industry events
• Internal tech talks and knowledge sharing
• Certification support (AWS, Azure, etc.)
🎯 Career Growth
• Quarterly performance reviews and goal setting
• Clear promotion pathways and criteria
• Leadership development opportunities
• Cross-functional project assignments