Key skills
PythonSQLBashPowerShellAIGenAIRAGMLOpsMLflowKubeflowPineconeWeaviateSnowflakeRedshiftDatabricksAWSAzureGCPTerraformKubernetesDockerECSEKSFargateAKSCloudWatchSageMakerAzure SQLPostgreSQLDatadogGitHubGitLabSaaSJiraConfluenceCI/CDWAF
About this role
Juvare is a SaaS software company focused on developing innovative enterprise resilience solutions for government agencies and healthcare providers. They are seeking a Senior DevOps Engineer to help build and improve secure cloud platforms for mission-critical solutions, partnering across Engineering and Security to deliver reliable SaaS environments.
Responsibilities:
- Help build, operate, and continuously improve the secure cloud platforms that power Juvare’s mission-critical enterprise resilience solutions
- Partner across Engineering and Security to deliver reliable, scalable SaaS environments for commercial and federal customers
- Support AI/MLOps workflows
Requirements:
- Strong hands-on experience with AWS and Azure
- Experience operating in FedRAMP High, DoD Impact Level 5 (IL5), or equivalent regulated environments
- Strong understanding of Authority to Operate (ATO) processes, compliance, and continuous monitoring (ConMon)
- Experience working within AWS GovCloud or secure boundary environments
- Operational experience with managed databases (backup/restore, replication, performance, and access management; Azure SQL and Aurora PostgreSQL preferred)
- Experience with infrastructure as code (Terraform)
- CI/CD pipeline experience (GitLab, GitHub)
- Experience building workflow automation and developer automation using tools like n8n, Replit, or similar
- Strong experience with containers and orchestration (Docker, Kubernetes, AKS, EKS, ECS/Fargate)
- Experience with monitoring and observability (Datadog, CloudWatch)
- Exposure to security tooling (WAF, SIEM, vulnerability management)
- Strong scripting skills (Python, Bash, Powershell)
- Experience in production operations (incident response, on-call, RCA)
- Strong understanding of networking, security, and system architecture
- Experience with MLOps pipelines (training, deployment, monitoring)
- Familiarity with tools like SageMaker, MLflow, Kubeflow, or similar
- Experience deploying models to production (real-time or batch)
- Understanding of data pipelines and model lifecycle management
- Experience with LLMs / GenAI workflows (RAG, prompt engineering, fine-tuning)
- Familiarity with vector databases (Pinecone, Weaviate, OpenSearch)
- Exposure to AIOps or AI-driven automation
- Experience with data platforms (Databricks, Snowflake, Redshift)
- GCP experience a plus; comfort operating with a cloud-agnostic mindset
- Multi-cloud or hybrid architecture experience
- Cost optimization and performance tuning
- Jira / Confluence experience