Opedia Technologies

Engineer

• Design and build virtualization infrastructure supporting GPU-intensive AI and HPC workloads.
• Develop and operate Kubernetes-based orchestration systems for GPU cluster provisioning and workload scheduling.
• Build automated provisioning systems that enable GPU capacity to be allocated, scaled, and reclaimed efficiently across multiple tenants.
• Design solutions for workload placement, resource management, and cluster lifecycle operations.
• Partner closely with hardware, networking, infrastructure, and AI platform teams to ensure orchestration systems align with real-world cluster architectures and constraints.
• Improve the reliability, security, scalability, and operational maturity of the orchestration platform.
• Build tooling and automation that simplifies infrastructure management and improves developer and customer experiences.
• Contribute to architectural decisions, engineering standards, and best practices as the platform evolves.

Virtualization & Orchestration Engineer

• Build and operate production-grade model-serving and inference systems supporting high-throughput, low-latency AI workloads.
• Optimize inference infrastructure for token throughput, latency, scalability, and cost efficiency across different model architectures and workloads.
• Design systems that maximize GPU utilization while maintaining predictable performance and reliability.
• Improve the scalability and operational maturity of inference platforms as customer demand grows.
• Partner with AI training, GPU performance, orchestration, and infrastructure teams to ensure smooth transitions from model development to production serving.
• Develop monitoring, alerting, and operational practices to maintain reliable inference services.
• Investigate and resolve performance, reliability, and capacity challenges across inference workloads.
• Contribute to architecture decisions and engineering standards as the platform evolves.

Inference Engineer

• Profile, analyze, and optimize GPU kernels to improve latency, throughput, and overall utilization.
• Identify and eliminate data-plane bottlenecks impacting GPU performance across large-scale AI workloads.
• Tune performance-critical workloads across training and inference environments.
• Work closely with AI infrastructure, machine learning, and platform engineering teams to understand workload characteristics and optimize system behavior.
• Develop benchmarking methodologies and performance measurement practices across GPU infrastructure.
• Evaluate emerging GPU technologies, performance tools, and optimization techniques as hardware platforms evolve.
• Contribute to engineering practices that improve GPU efficiency, scalability, and reliability across the fleet.

GPU Performance / Kernel Engineer

Infrastructure Engineer

• Build and scale distributed training infrastructure supporting large AI models across large GPU clusters.
• Design and improve systems that increase training reliability, efficiency, and resource utilization.
• Develop solutions for fault tolerance, checkpointing, recovery, and large-scale training operations.
• Integrate AI models into production training pipelines in partnership with platform, orchestration, and performance engineering teams.
• Diagnose and resolve issues impacting training throughput, stability, reliability, and cost efficiency.
• Build tools and automation that improve the developer experience for AI researchers and engineers.
• Establish best practices for training infrastructure, operational processes, and platform reliability.
• Contribute to the evolution of the AI infrastructure platform as an early member of the engineering team.

AI Training Infrastructure Engineer

Mechanical Engineer

• Lead the design and configuration of GPU servers and rack-scale infrastructure using vendor reference architectures from NVIDIA, AMD, and emerging AI hardware providers.
• Partner closely with ODMs and OEMs to develop, validate, and optimize custom hardware platforms for production deployment.
• Own rack-level architecture, including mechanical layout, power distribution, thermal design, cable management, serviceability, and deployment standards.
• Evaluate new GPU platforms, server technologies, and hardware innovations to determine their suitability for next-generation AI workloads.
• Collaborate with data center engineering, networking, infrastructure, and operations teams to ensure seamless integration across facilities and production environments.
• Balance performance, reliability, manufacturability, scalability, and total cost of ownership when designing infrastructure solutions.
• Support hardware validation, qualification, and production readiness for large-scale deployments.
• Help establish engineering standards, hardware specifications, and design best practices as the infrastructure organization grows.

Hardware – Machine Design Engineer

Data Engineer

• Build, lead, mentor, and grow high-performing Operations & Maintenance teams.
• Develop the operational strategy, organizational structure, and execution model supporting large-scale AI infrastructure.
• Establish world-class operational processes, including incident response, change management, problem management, and service reliability.
• Lead major incident management efforts and executive communications during production events.
• Drive operational excellence through proactive monitoring, observability, automation, and continuous improvement initiatives.
• Partner with Engineering to ensure operational readiness for new infrastructure deployments and platform launches.
• Define service level objectives (SLOs), operational KPIs, and reliability metrics across the infrastructure portfolio.
• Build scalable on-call programs, escalation models, runbooks, and operational governance.
• Champion root cause analysis and long-term corrective actions to improve platform resilience.
• Influence infrastructure architecture and operational tooling to improve availability, efficiency, and customer experience.
• Help shape the long-term operations organization as the company expands globally.

Data Center Operations and Maintenance Engineering Leader

• Monitor live data center and GPU infrastructure for performance, capacity, and reliability issues.
• Lead or support incident response for production issues, driving toward fast, effective resolution.
• Collaborate with the engineering teams to make sure monitoring, alerting, and operational tooling are first class and make issues are caught before they impact customers.
• Partner with hardware, networking, orchestration, and infrastructure teams to resolve root causes and prevent recurrence.
• Contribute to runbooks, on-call practices, and operational maturity as the platform scales.

Data Center Operations and Maintenance Engineer

• Design, build, automate, and operate data center networking supporting large-scale GPU clusters, AI workloads, and multi-tenant cloud services.
• Develop secure tenant isolation architectures that ensure performance, scalability, and security across customer environments.
• Design resilient, highly available network architectures across compute, storage, and infrastructure services.
• Work across multiple networking platforms and vendors, including technologies from Arista, Juniper, HPE, and other enterprise networking providers.
• Partner closely with hardware, infrastructure, virtualization, and platform engineering teams to optimize networking for AI training and inference workloads.
• Build infrastructure automation to simplify deployment, configuration management, monitoring, and operational excellence.
• Define networking standards, architecture documentation, and engineering best practices as the infrastructure organization grows.
• Evaluate emerging networking technologies and help shape the long-term evolution of the company's AI networking platform.

Data Center Network Engineer

Project Manager

• Manage and deliver utility infrastructure projects, including drinking water, wastewater, and stormwater systems.
• Develop project scopes, schedules, budgets, and proposals.
• Coordinate with clients, subconsultants, regulatory agencies, and internal teams.
• Monitor project performance, including budget, schedule, quality, and risk.
• Ensure QA/QC compliance for all project deliverables and documentation.
• Track labor utilization, revenue forecasting, billing, and documentation.
• Serve as the firm’s representative at client meetings, public hearings, and presentations.
• Conduct site visits and construction observations as needed.
• Lead multiple utility infrastructure projects from concept to completion, ensuring schedule, budget, and quality targets are met.
• Mentor engineers and project managers, providing technical guidance and career development support.

Senior Project Manager

• Manage utility infrastructure projects including drinking water, wastewater, and stormwater systems.
• Develop project scopes, schedules, budgets, and proposals.
• Coordinate with clients, subconsultants, regulatory agencies, and internal teams.
• Supervise project staff and mentor engineers/technicians under senior guidance.
• Perform QA/QC reviews, address design issues, and assist with construction-related problems.
• Track labor utilization, revenue forecasting, billing, and documentation.
• Conduct occasional site visits and construction observations.

• Manage and deliver utility infrastructure projects, including drinking water, wastewater, and stormwater systems.
• Develop project scopes, schedules, budgets, and proposals.
• Coordinate with clients, subconsultants, regulatory agencies, and internal teams.
• Monitor project performance, including budget, schedule, quality, and risk.
• Ensure QA/QC compliance for all project deliverables and documentation.
• Track labor utilization, revenue forecasting, billing, and documentation.
• Serve as the firm’s representative at client meetings, public hearings, and presentations.
• More senior PMs will also conduct site visits and construction observations as needed.
• Lead multiple utility infrastructure projects from concept to completion, ensuring schedule, budget, and quality targets are met.
• Mentor engineers and project managers, providing technical guidance and career development support.

Technical Product Manager

• Define and manage the platform product roadmap, balancing strategic initiatives with business priorities and technical requirements.
• Partner closely with engineering, data science, and business stakeholders to deliver scalable, high-impact platform capabilities.
• Lead platform modernization and innovation efforts focused on scalability, reliability, performance, and extensibility.
• Translate technical requirements into clear product specifications, user stories, and acceptance criteria.
• Drive API strategy and evolution, ensuring a seamless developer experience and robust integration capabilities.
• Collaborate with executive leadership, product teams, and customer-facing stakeholders to align platform investments with company objectives.
• Operate within Agile and Kanban environments, participating in planning, backlog management, and delivery processes.
• Identify technical risks and proactively develop mitigation strategies related to scalability, system availability, and integrations.
• Support sales, onboarding, and customer success teams by providing platform expertise and product guidance.
• Evaluate emerging technologies, market trends, and competitive developments to inform future platform strategy.
• Partner with AI and autonomous systems teams to create product requirements that support agent-driven engineering workflows and AI-enabled delivery models.

Virtualization & Orchestration Engineer

Inference Engineer

GPU Performance / Kernel Engineer

AI Training Infrastructure Engineer

Hardware – Machine Design Engineer

Data Center Operations and Maintenance Engineering Leader

Data Center Operations and Maintenance Engineer

Data Center Network Engineer

Senior Project Manager

Project Manager

Senior Project Manager

Technical Product Manager – Platform and Innovation