Designworks Talent LLC

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

Research Scientist

• Drive research, development, and refinement of generative AI and deep learning models
• Collaborate with a diverse team of scientists, engineers, and product leaders
• Build scalable solutions for real-world challenges in fraud detection and identity verification

Principal Applied Scientist

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

• 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

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

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

Recruitment

• Run a full recruiting desk, including client acquisition, relationship management, and candidate delivery
• Own your client relationships and manage searches from intake through placement
• Collaborate with other senior recruiters on shared searches, role-splits, or candidate pipelines
• Exchange market insights, sourcing strategies, and best practices with peer recruiters
• Leverage shared recruiting technology, ATS, and AI-driven tools without building infrastructure alone
• Contribute strategically to a growing recruiter network built on trust and performance