DevOps and AI Automation Engineer

Company Description Murphi.ai is an AI automation platform focused on healthcare workflows across Home Health, Hospice, Home Care, Mental & Behavioral Health, IDD, and Primary & Specialist Care. For more details see: https://Murphi.ai Role Description Engineer - DevOps & AI Automation Location: United States (Prefer East Coast either in NC or PA) Experience: 0–3 Years Employment: Full-Time | Entry Level / Early Career OPT / STEM OPT: Candidates are welcome to apply Reports To: Global Head – DevOps, Security & Architecture and U.S. AI Platform Lead About the Role We are looking for a DevOps & AI Automation Engineer to join our U.S. technology team. This is a hands-on engineering role with two primary areas of responsibility: DevOps & Platform Reliability – managing our GCP/GKE infrastructure and ensuring reliable operation of our production platform.AI Platform & Workflow Automation – working with our U.S.-based AI Platform Lead to build, test, deploy, and improve AI-powered healthcare workflows.The ideal candidate is an early-career engineer who wants to develop expertise at the intersection of Cloud, Kubernetes, DevOps, Generative AI, and Healthcare AI Automation. Key Responsibilities DevOps & Platform Operations Maintain and monitor our Google Cloud Platform (GCP) and Google Kubernetes Engine (GKE) infrastructure supporting U.S. and India operations.Manage Kubernetes workloads, deployments, containers, APIs, configurations, scaling, and production environments.Monitor platform health through Grafana, GCP monitoring, logs, metrics, and alerts.Troubleshoot production issues across infrastructure, APIs, backend services, networking, and applications.Ensure high availability and rapid resolution of issues so customer operations are never stranded because of platform failures.Support CI/CD, infrastructure automation, security, backups, disaster recovery, and cloud optimization.Perform root-cause analysis and automate recurring DevOps activities. AI Platform & Healthcare Workflow Automation Work closely with the AI Platform Leads to develop and operate AI-powered platform workflows.Build and test AI automation for healthcare clinical, administrative, documentation, compliance, and operational workflows.Work with LLMs, AI APIs, AI agents, workflow orchestration, document processing, and structured/unstructured healthcare data.Develop AI workflow prototypes, simulations, integrations, and production implementations.Help deploy and monitor AI workflows in production and improve their accuracy, reliability, latency, scalability, and cost efficiency.Integrate AI workflows with backend APIs and healthcare systems.Extensively use Codex and other AI-assisted engineering tools for coding, debugging, testing, automation, log analysis, and DevOps operations. Required Skills Strong foundational knowledge of Google Cloud Platform (GCP) – required.Knowledge of Kubernetes/GKE, Docker, Linux, APIs, and cloud infrastructure.Familiarity with Grafana, monitoring, logging, and observability.Programming/scripting experience in Python, JavaScript/TypeScript, Java, Go, Bash, or similar.Understanding of REST APIs, backend services, databases, Git, and CI/CD.Interest or hands-on experience with Generative AI, LLMs, AI APIs, agents, and workflow automation.Strong troubleshooting and problem-solving skills.Ability to take ownership of production issues through resolution. Key Metrics Platform Uptime | GKE Health | API Latency & Error Rates | Deployment Success | MTTD | MTTR | Incident Frequency | AI Workflow Reliability | AI Processing Latency | Cloud/AI Cost Efficiency Qualifications Master's degree in Computer Science, Software Engineering, AI/ML, Data Science, Cloud Computing, or related STEM discipline.0–3 years of experience.Fresh graduates with strong GCP, Kubernetes, software development, or AI projects are encouraged to apply.Internships, academic projects, and hands-on cloud/AI experience will be considered.OPT and STEM OPT candidates are welcome to apply. Growth Opportunity DevOps → GCP/Kubernetes → Platform Engineering → Generative AI → AI Agents → Healthcare Workflow Automation → AI Platform Architecture The ideal candidate enjoys building, automating, troubleshooting, and owning production systems and wants to grow into a strong DevOps + AI Platform Lead Engineer.