Forward Deployed Infrastructure Engineer

Optura

14d•Remote

About The Position

Optura is healthcare’s AI orchestration platform. We help healthcare organizations transform disconnected AI pilots into a unified, enterprise-scale program that delivers measurable value. Our platform enables teams to design, execute, and monitor intelligent agents that drive automation, insights, and action—while providing the control and observability needed to scale safely. Built for real-world complexity, Optura supports multiple model providers, integrates seamlessly with existing infrastructure, and offers both SaaS and self-hosted options. Our mission: revolutionize how healthcare deploys and operationalizes AI in production. We are seeking an Infrastructure & DevOps Forward Deployed Engineer to help customers successfully deploy, integrate, and operationalize Optura’s AI platform within complex enterprise environments. This is a highly hands-on, customer-facing role that blends cloud infrastructure, DevOps, and applied AI implementation. You will work directly with healthcare organizations including insurance, hospital systems, and healthtech companies to configure environments, deploy the platform, and build scalable, production-grade solutions. You will serve as a critical bridge between customers and internal product and engineering teams—bringing customer requirements to life while informing platform improvements. This role is ideal for someone who enjoys working across infrastructure and application layers, thrives in dynamic environments, and wants to have a direct impact on how AI is deployed in real-world healthcare settings.

Requirements

4+ years of experience in cloud infrastructure, DevOps, or software engineering roles
Hands-on experience with core cloud services across one or more major providers (AWS, GCP, and/or Azure).
This includes compute and container orchestration platforms (EC2, Compute Engine, Virtual Machines, EKS, GKE, AKS),
object storage solutions (S3, Cloud Storage, Blob Storage),
identity and access management frameworks (AWS IAM, Cloud IAM, Microsoft Entra ID/Azure RBAC),
managed relational database services (RDS, Cloud SQL, Azure SQL Database),
and cloud monitoring and logging tools (CloudWatch, Cloud Monitoring/Logging, Azure Monitor).
Familiarity with the architectural differences and best practices across cloud platforms is a strong plus.
Experience with Kubernetes, Docker, and containerized application deployment
Experience building CI/CD pipelines and using Infrastructure-as-Code tools such as Terraform or CloudFormation
Experience working in customer-facing technical roles such as forward deployed engineer, solutions engineer, or implementation engineer

Nice To Haves

You’ve shipped end to end– you can own a feature from problem definition to production, with AI as a core tool in your workflow
AI-native: you don’t use AI tools occasionally– they’re built into how you work
Experience deploying AI, machine learning, or data platforms in production environments
Experience working in healthcare, healthtech, or other regulated industries (HIPAA, SOC2)
Experience integrating enterprise systems within complex customer environments
Proficiency in Python, JavaScript, or similar languages used for platform integration and development
Experience with observability tools such as Prometheus, Grafana, or similar

Responsibilities

Partner with customer engineering and business teams to gather requirements and translate them into scalable infrastructure and platform solutions
Lead deployment, configuration, and integration of Optura’s platform within customer cloud environments (primarily AWS)
Build and manage cloud infrastructure, including Kubernetes clusters, containerized services, and networking configurations
Develop and maintain Infrastructure-as-Code and CI/CD pipelines to support reliable, repeatable deployments
Configure and optimize AI platform components, including orchestration, agent workflows, and retrieval-augmented generation (RAG) pipelines
Build and deploy customer-specific AI use cases, applying software engineering best practices across the full development lifecycle
Collaborate with product and engineering teams to communicate customer feedback and influence platform evolution
Monitor system performance, analyze metrics, and continuously improve reliability, scalability, and cost efficiency