• Design pick, pack, receiving, and RMA workflows for each site's warehouse layout, optimizing for travel distance, throughput, and dock-to-floor time. • Run time and motion studies on warehouse and dock processes, and use the data to set the standard work each site trains against. • Model rack, dock door, and staging layouts for new and existing sites, balancing throughput against real estate cost. • Build the process documentation and standard work instructions that let a new site ramp to full productivity in weeks, not months. • Partner with the WMS and IMS system build teams to make sure new tooling matches the physical workflow, not the other way around.

• Monitor water treatment plant parameters (e.g., pH, turbidity, flux, flow rates, and chemical dosing) using SCADA, IoT, or cloud-based monitoring platforms. • Analyze operational data to identify opportunities for process improvement, cost reduction, energy savings, and enhanced treatment performance. • Perform real-time adjustments to process controls, alarm settings, and automation systems to maintain optimal treatment performance. • Develop process control strategies and operational best practices for reverse osmosis treatment systems. • Conduct root cause analyses of operational issues and recommend corrective actions to customer plant personnel. • Support the startup, commissioning, troubleshooting, and performance testing of treatment systems. • Develop standard operating procedures (SOPs), dashboards, key performance indicators (KPIs), technical reports, and reporting tools to track facility performance and operational trends. • Establish alarm management protocols and escalation procedures. • Ensure continuous monitoring coverage and timely response to critical system events. • Drive continuous improvement of remote monitoring capabilities and supporting technologies. • Analyze operational, process, and asset performance data to identify trends and optimization opportunities. • Develop predictive monitoring and performance management tools. • Use monitoring data to anticipate equipment failures and proactively schedule maintenance. • Utilize statistical analysis and process modeling tools to support operational decision-making. • Provide technical guidance and support to engineering, rental, and site services teams. • Serve as the subject matter expert for treatment processes and process control systems. • Conduct operator training on process optimization and monitoring systems. • Establish performance metrics and operational objectives for the remote monitoring program. • Collaborate with the Chief Technology Officer to develop program enhancements and machine learning capabilities for the remote monitoring program. • Other duties as assigned.

Role Description We are looking for an Integration Engineer with expertise in Microsoft Power Platform and enterprise integrations to help build scalable, secure, and intelligent solutions for our clients. In this role, you will: - Design and develop integrations between Microsoft Copilot, Power Platform, and enterprise applications, enabling seamless connectivity and data exchange across business systems. - Collaborate with cross-functional teams to deliver modern integration solutions that support AI-powered experiences and enterprise digital transformation. Qualifications - Strong understanding of enterprise integration patterns, API management, authentication, and security protocols (OAuth 2.0, Azure AD, JWT). - Professional proficiency in English. Requirements - Design, develop, and maintain enterprise integrations between Microsoft Copilot, Power Platform, and business applications. - Build, customize, and manage Power Platform connectors to enable secure and scalable integrations. - Design and develop RESTful APIs and integration services following modern architecture best practices. - Support Microsoft Copilot connectivity with enterprise systems and data sources. - Enable secure, reliable, and high-performing enterprise data flows across multiple platforms. - Develop integration solutions using Microsoft Power Platform, including Power Automate and related services. - Experience with API development, web services, and event-driven architectures. - Familiarity with cloud platforms, preferably Microsoft Azure Integration Services. - Experience working with source control, CI/CD pipelines, and Agile development methodologies. - Strong problem-solving skills and the ability to collaborate effectively with distributed engineering teams. Benefits - Designing modern integration solutions that power enterprise-scale digital transformation. - Working with cutting-edge Microsoft technologies, including Copilot, Power Platform, and Azure. - Collaborating with global engineering teams to solve complex integration challenges. - Building secure, scalable architectures that connect people, applications, and data. - Helping organizations unlock the full potential of AI and automation through intelligent integrations. - Being part of a winning squad that plays to each other's strengths and celebrates every success together. Bonus Points - Experience with Model Context Protocol (MCP). - Experience integrating AI-powered solutions, including Microsoft Copilot. - Familiarity with Azure API Management, Azure Logic Apps, Azure Functions, or Azure Service Bus. - Experience integrating with Microsoft Graph API and Microsoft 365 services. - Knowledge of enterprise security, identity management, and governance best practices. - Microsoft Power Platform or Azure certifications. - Experience working in consulting or enterprise client environments.

Role Description We are seeking a Model Serving Engineer to design, build, and operate high-performance, highly reliable inference platforms for serving large machine learning models in production. The role focuses on the systems engineering side of AI deployment, including: - Request routing - Batching - Caching - Autoscaling - GPU utilization - End-to-end observability across diverse model workloads The ideal candidate brings strong distributed systems and performance engineering expertise, has shipped serving systems at scale, and understands the trade-offs between latency, throughput, cost, and quality in ML serving. Qualifications - Bachelor’s or Master’s degree in Computer Science or a related field. - Six or more years of experience in distributed systems, infrastructure, or ML platform engineering. - Strong proficiency in Python and a systems language such as Go, Rust, or C++. - Deep experience operating high-throughput, low-latency services in production. - Hands-on experience with LLM or large model inference frameworks such as vLLM or TensorRT-LLM. - Strong understanding of GPU architecture, memory hierarchies, and accelerator utilization. - Familiarity with Kubernetes, autoscaling, and modern cloud platforms. - Experience with observability stacks including metrics, tracing, and structured logging. - Solid grounding in performance engineering and capacity planning. - Strong communication and incident response skills. Requirements - Design and operate model serving platforms supporting diverse workloads including LLMs, vision models, and recommendation systems. - Optimize inference performance using continuous batching, paged attention, speculative decoding, and request multiplexing. - Implement multi-tenant routing, rate limiting, and quality-of-service policies across model endpoints. - Build autoscaling and capacity management systems that balance latency, throughput, and cost. - Tune GPU utilization, memory management, and KV cache strategies for LLM serving workloads. - Integrate model serving with API gateways, identity systems, and observability platforms. - Implement caching, prompt deduplication, and response reuse strategies where appropriate. - Drive end-to-end observability including latency histograms, queue dynamics, GPU utilization, and error tracking. - Develop deployment workflows including canary releases, shadow testing, and automated rollback. - Operate incident response for high-availability AI services and drive durable reliability improvements. - Collaborate with ML and product teams to support new model releases and capability rollouts. - Implement security controls including request signing, content filtering, and abuse detection at the serving layer. - Document operational procedures, performance characteristics, and tuning guidance for internal teams. - Stay current with AI serving research and translate advances into production capabilities. Preferred Qualifications - Open-source contributions to model serving infrastructure. - Experience with multi-region or globally distributed AI serving. - Familiarity with model quantization, distillation, and compression techniques. - Exposure to FinOps for AI workloads and cost-efficient serving design. - Experience supporting external-facing AI APIs at scale. Benefits - Competitive base salary commensurate with experience, plus benefits. How to Apply Would you like to know more about this opportunity? For immediate consideration, please send your resume to [email protected] . Learn more about Bright Vision Technologies at www.bvteck.com .