Role Description We’re looking for a Mid-Level DevOps / Systems Engineer who is excited about both traditional infrastructure and emerging AI/ML platforms. You will report to our Engineering Manager and help build and operate the reliable, scalable AWS foundation that powers our core platform and our growing AI features (using Amazon Bedrock, Anthropic Claude, and similar tools). This is a hands-on role with room to grow: you’ll own meaningful projects, collaborate closely with backend and data teams, and help shape how we productionize AI workloads. Responsibilities - Provision, and maintain scalable infrastructure on AWS using Terraform. - Deploy and manage containerized applications with Docker, Kubernetes, and AWS Fargate. - Write automation scripts and tools in Python and Bash. - Build and optimize CI/CD pipelines for both backend services and AI components using GitHub Actions. - Integrate and operate AI/ML services: - Amazon Bedrock (model invocation, agents, knowledge bases, guardrails). - Anthropic Claude models via Bedrock or direct API. - Monitor performance, cost, and reliability of both traditional and AI workloads (CloudWatch, Prometheus, custom dashboards). - Ensure security, compliance, and responsible AI practices (data privacy, model safety, cost controls). - Troubleshoot production issues and contribute to on-call rotation. - Collaborate on infrastructure improvements that support AI-driven features (e.g., real-time shift recommendations, chat assistants, predictive no-show detection). Qualifications - 3–5 years of hands-on DevOps / Systems Engineering experience. - Solid experience with AWS core services (EC2, Fargate, VPC, IAM, S3, RDS, CloudWatch). - Working knowledge of Terraform (or similar IaC). - Proficiency with Docker and container orchestration (Kubernetes or ECS). - Comfortable scripting in Python and Bash. - Understanding of AI/ML operational concerns: latency, cost optimisation, model versioning, prompt engineering basics, and guardrails. - Good grasp of networking, security, and observability best practices. - Strong problem-solving skills and eagerness to learn. Requirements - Experience with CI/CD tools (GitHub Actions, CodePipeline, ArgoCD). - Exposure to real-time or high-volume SaaS systems. - Basic knowledge of data pipelines or event-driven architecture (EventBridge, SQS, Kafka). - Practical experience integrating Amazon Bedrock and/or Anthropic Claude. Benefits - Less-formal work environment. - Competitive compensation and benefits offering.

• You will be part of an DGX Cloud team responsible for production systems that enable large scalable GPU clusters to be used for a variety of AI workloads. • This includes working on custom software related to scheduling GPU resources on kubernetes. • Implementing monitoring and health management capabilities that enable industry leading reliability, availability, and scalability of GPU assets. • You will be harnessing multiple data streams, ranging from GPU hardware diagnostics to cluster and network telemetry. • Working with teams across NVIDIA to ensure production AI clusters run reliability and consistently with maximum performance. • Evaluating system failures and improving services based on a well-defined incident management process.

Role Description The Principal Engineer applies engineering skills to solve complex problems, using knowledge of Ecolab's customers and markets. He or she schedules and helps to coordinate innovative, complex projects to meet cost and time objectives, complying with design criteria and standards. What’s in it For You - Thrive in a company that values a culture of safety to include top-notch safety training and personal protection equipment. - Comprehensive benefits package starting day 1 of employment - medical, dental, vision, matching 401k, company paid pension, stock purchase plan, tuition reimbursement and more! What You Will Do - Train and motivate a highly competent, flexible, and efficient work force to meet plant needs in a safe manner, strongly promoting employee involvement. - Provide maintenance support for maintaining customer levels. - Assure that housekeeping and safety standards are maintained in accordance with company standards. - Control wastewater flow to comply with the “closed drains” operational philosophy. - Oversee all electrical, mechanical and facility maintenance and preventative maintenance, including grounds and building. - Manage plant maintenance work order program. - Maintain labor and outside charges within budget. - Manage and control the plant capital expenditures budget. - Oversee facility preventative maintenance program. - Review purchase orders submitted for grounds, utilities, building supplies, and other related areas. - Coordinate with corporate engineering in establishing and installing capital budget programs. - Interact with outside officials to maintain plant regulatory compliance. - Participate on the plant environmental/compliance team. - Support Plant OSHA compliance via membership on the Central Safety Committee, performing required safety audits, documentation of required planned inspections and quick follow-up and response to safety observations that are submitted to maintenance. - Participate in community events. - Submit and implement TQIP’s to reduce plant costs. - Perform production supervisor responsibilities to substitute for Production Supervisors in their absence. Qualifications - Bachelor’s degree in Chemical, Mechanical or Electrical Engineering. - 5 years’ directly related engineering experience. - 1 year experience with TPM. - No immigration sponsorship available for this position. Preferred Qualifications - Advanced degree in an engineering discipline. - Demonstrated knowledge of project management tools and techniques on cross functional teams. - Ability to guide and train lower level engineers. - Demonstrated competence in the management of one or more engineering project concurrently, with emphasis on in-depth technical contribution. - Current with technology in his/her discipline.

• You will be part of a DGX Cloud team responsible for production systems that enable large scalable GPU clusters to be used for a variety of AI workloads. • Designing and developing a massively distributed scalable platform which would be used to identify, diagnose and remediate non-performant GPU assets. • Working with teams across NVIDIA to ensure production AI clusters run reliably and consistently with maximum performance. • Evaluating system failures and improving services based on a well-defined incident management process.