Staff DevOps Engineer

Responsibilities

• Infrastructure Development & Integration
• Design, implement, and manage cloud-native infrastructure (AWS, Azure, GCP) to support healthcare platforms, AI agents, and clinical applications.
• Build and maintain scalable CI/CD pipelines to enable rapid and reliable delivery of software, data pipelines , and AI/ML models.
• Design and manage Kubernetes (K8s) clusters for container orchestration, workload scaling, and high availability with integrated monitoring to ensure cluster health and performance
• Implement Kubernetes-native tools (Helm, Kustomize, ArgoCD) for deployment automation and environment management ensuring observability through monitoring dashboards and alerts
• Collaborate with Staff Engineers/Architects to align infrastructure with enterprise goals for scalability, reliability, and performance leveraging monitoring insights to inform architectural decisions.
• System Optimization & Reliability
• Implement and maintain comprehensive monitoring, logging, and alerting mechanisms (Prometheus, Grafana, ELK, Datadog, AWS cloudwatch, AWS cloud trail) to ensure real-time visibility into system performance, resource utilization, and potential incidents.
• Implement monitoring, logging, and alerting mechanisms (Prometheus, Grafana, ELK, Datadog) to ensure system reliability and proactive incident response.
• Ensure data pipeline workflows (ETL/ELT, real-time streaming, batch processing) are observable, reliable, and auditable.
• Support observability and monitoring of GenAI pipelines, embeddings, vector databases, and agentic AI workflows .
• Proactively analyze monitoring data to identify bottlenecks, predict failures, and drive continuous improvement in system reliability.
• Compliance & Security
• Support audit trails and compliance reporting through automated DevSecOps practices.
• Implement security controls for LLM-based applications, AI agents, and healthcare data pipelines , including prompt injection prevention, API rate limiting, and data governance.
• Collaboration & Agile Practices
• Partner closely with software engineers, data engineers, AI/ML engineers, and product managers to deliver integrated, secure, and scalable solutions.
• Contribute to agile development processes including sprint planning, stand-ups, and retrospectives.
• Mentor junior engineers and share best practices in cloud-native infrastructure, CI/CD, Kubernetes, and automation.
• Innovation & Technical Expertise
• Stay informed about emerging DevOps practices, cloud-native architectures, MLOps/LLMOps , and data engineering tools.
• Prototype and evaluate new frameworks and tools to enhance infrastructure for data pipelines, GenAI, and Agentic AI applications .
• Advocate for best practices in infrastructure design, focusing on modularity, maintainability, and scalability.

Requirements

• Education & Experience
• Bachelor's or Master's degree in Computer Science, Engineering, or related technical discipline.
• 10+ years of experience in DevOps, Site Reliability Engineering, or related roles, with at least 3+ years building cloud-native infrastructure.
• Proven track record of managing production-grade Kubernetes clusters and cloud infrastructure in regulated environments.
• Experience supporting GenAI/LLM applications (e.g., OpenAI, Hugging Face, LangChain) and vector databases (e.g., Pinecone, Weaviate, FAISS).
• Hands-on experience supporting data pipeline products using ETL/ELT frameworks (Apache Airflow, dbt, Prefect) and streaming systems (Kafka, Spark, Flink).
• Experience deploying AI agents and orchestrating agent workflows in production environments.
• Technical Proficiency
• Expertise in Kubernetes (K8s) for orchestration, scaling, and managing containerized applications.
• Strong proficiency in containerization (Docker) and Kubernetes ecosystem tools (Helm, ArgoCD, Istio/Linkerd for service mesh).
• Hands-on experience with Infrastructure as Code (Terraform, CloudFormation, or Pulumi).
• Proficiency with CI/CD tools (Jenkins, GitHub Actions, GitLab CI, ArgoCD, Spinnaker).
• Familiarity with monitoring and observability tools (Prometheus, Grafana, ELK, Datadog, AWS cloud watch and AWS cloud trail), including setting up dashboards, alerts, and custom metrics for cloud-native and AI systems.
• Good to have: knowledge of healthcare data standards (FHIR, HL7) and secure deployment practices for AI/ML and data pipelines.
• Professional Skills
• Strong problem-solving skills with a focus on reliability, scalability, and security.
• Excellent collaboration and communication skills across cross-functional teams.
• Proactive, detail-oriented, and committed to technical excellence in a fast-paced healthcare environment.
• About Get Well:

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