Site Reliability Engineer

Job Title : Site Reliability Engineer

Duration: Initial 3–6 months, C2H

Location: Irvine, CA- Onsite, 5 days per week

Info Required to Submit

• Updated resume
• Candidate location & onsite confirmation
• Years of experience in AWS, Kubernetes, Terraform, CI/CD
• SRE/DevOps experience summary

About the Role

The SRE will embed directly with product teams to support platform reliability, troubleshoot issues, and act as the bridge between development, platform engineering, and cloud infrastructure. This is not a coding-heavy role and focuses on systems understanding, troubleshooting, Kubernetes operations, cloud infrastructure, CI/CD pipelines, and production stability. The team is newly formed and supports product-facing platform needs after splitting from core cloud/DevOps engineering.

Seniority Level / Target Experience

Mid-level (4–6 years). Not junior; not senior/staff-level.

Top 3–5 Technical Skills

1. AWS (strong hands‑on production experience)
2. Kubernetes (cluster ops, configuration, debugging)
3. Terraform (IaC)
4. CI/CD & DevOps (pipelines, deployments, automation)
5. Observability fundamentals (Grafana, logs/metrics/traces)

Bonus (Not Required):

• Kafka / Streaming
• MLOps / AI infrastructure
• Event-driven or ML-driven platform experience
• Platform ownership post-vendor POC

Timeline to Start / Fill

Immediate need; start as soon as they identify a strong candidate.

Interview Process

3 Rounds

1. Hiring Manager + Lead Engineer – Preliminary technical screen
2. Full Technical Panel (~1 hour) – Deep technical problem-solving & troubleshooting
3. Culture / Team Fit – Prefer onsite if local; remote OK if relocating

Additional Info

• Bachelor’s degree preferred but equivalent experience accepted
• Strong troubleshooting skills across distributed systems
• Must be comfortable in a high-change, incident-driven environment
• Will participate in on-call support and incident response
• Role sits on a new Product Platform team (separate from core DevOps/cloud)
• Focus is platform reliability, not feature development
• Must be highly organized, detail-oriented, and able to work independently

Site Reliability Engineer (SRE) – Product Platform Team

Role Overview

As a Site Reliability Engineer, you will be immersed in a high-performing and frequently challenged team responsible for the reliability, scalability, and operational excellence of PDS’s product platform. You will work and consult closely with engineering and product teams to design, build, and operate resilient platform services, implement automation and reliability frameworks, and ensure the availability and performance of shared platform capabilities. You will also be responsible for the ongoing operation, monitoring, and continuous improvement of platform infrastructure and services that support all product teams.

Responsibilities

Must Have

• Cloud Architecture & Infrastructure Engineering
  • Architect and maintain multi-cloud infrastructure (AWS and GCP and Azure) to support enterprise-scale healthcare operations.
  • Implement Infrastructure-as-Code (IaC) using Terraform, Helm charts, and CloudFormation to automate resource provisioning and ensure consistency across environments.
  • Configure, manage, and deploy Kubernetes clusters and cloud native tooling according to best practices for scaling, resiliency, and reliability.
  • Manage and optimize multi-cluster Kubernetes environments, utilizing Istio service mesh for advanced traffic management, service discovery, and observability.
  • Enforcing security standards and SAST, DAST, code quality scans on the Kubernetes cluster and application pipelines and containers and remediating any security findings related to the platform.
  • Design solutions that are aligned with business value in regard to TCO and ROI.
  • Design cross‑region disaster recovery strategies and rolling deployment architectures to ensure high availability and business continuity for mission‑critical applications.

DevOps & Automation

• Develop and maintain CI/CD pipelines using BitBucket Pipelines and ArgoCD, leveraging GitOps practices for automated testing and deployment.
• Develop and implement database deployment pipelines using Liquibase
• Create automation for infrastructure and application configuration using Ansible
• Implement automated health validation and failover capabilities to facilitate zero‑downtime updates.
• Provide technical mentorship to engineering teams on event‑driven architecture, streaming best practices, and DevOps methodologies.

Security, Compliance & Observability

• Design comprehensive monitoring and observability solutions using Prometheus, Grafana, and OpenTelemetry for distributed tracing and system performance visibility.
• Implement robust security architectures using Conjur Cloud for automated credential rotation and default‑deny network policies.
• Configure advanced authentication mechanisms, including SASL/SSL with SCRAM‑SHA‑512, and manage RBAC permissions for database clusters.
• Ensure infrastructure adherence to regulatory compliance frameworks (HIPAA, SOC 2, ISO 27001) through automated policy enforcement and encrypted communication channels.
• Building “Golden Signal” dashboards (Latency, Traffic, Errors, Saturation) that automatically populate for every new microservice.
• Create automation to reduce TOIL and auto healing for remediations within monitoring
• Track application and platform costs from all layers and optimize costs on the platform.

Nice to Have

Event Streaming & Data Processing

• Architect real‑time stream processing applications using Apache Flink with high‑availability configurations, incorporating RocksDB state management and persistent storage.
• Build and manage event streaming infrastructure using Apache Kafka (Amazon MSK) and Kafka Connect, ensuring seamless data ingestion and integration.
• Implement Debezium CDC (Change Data Capture) for real‑time synchronization of MongoDB data streams.
• Manage Confluent Schema Registry to support Avro, JSON, and Protobuf schemas, ensuring backward compatibility across data pipelines.
• Automate data pipelines and orchestration using Dagster and cloud‑native data services.

MLOps, AI Infrastructure

• Design and implement AI platform architecture to support MCP and agents using cloud native tools and security best practices
• Establish and maintain MLOps pipelines using MLflow and Kubeflow to support the training, deployment, and monitoring of machine learning models.
• Design infrastructure to support Generative AI (Gen‑AI) and Large Language Models (LLM), specifically regarding Retrieval‑Augmented Generation (RAG) implementations.
• Create monitoring systems around AI applications to ensure performance and accuracy
• Configure and deploy vector database to maintain scalability and indexing performance for RAG based AI workloads
• Implement security and guardrails around AI workloads

Qualifications

Required

• BS degree in Computer Science or related field plus 4 years of relevant technology experience or equivalent combination of education & education & experience in lieu of degree, 4+ years of relevant expertise is required.
• Hands‑on experience supporting production‑grade cloud infrastructure in at least one major cloud provider (AWS, GCP, or Azure).
• Practical experience operating and maintaining Kubernetes‑based platforms in production environments.
• Experience with Infrastructure as Code (IaC) tools such as Terraform, Helm, or CloudFormation.
• Working knowledge of CI/CD and GitOps practices, including automated testing and deployment pipelines.
• Experience implementing or supporting monitoring, alerting, and observability solutions (metrics, logs, traces).
• Strong troubleshooting skills across distributed systems, including performance, availability, and reliability issues.
• Proficiency in at least one scripting or programming language (e.g., Python, Go, Bash).
• Experience participating in on‑call rotations, incident response, and root cause analysis

Preferred

• Experience operating multi‑cloud environments (AWS, GCP, Azure).
• Experience with event streaming platforms such as Apache Kafka, Kafka Connect, or managed services (e.g., Amazon MSK).
• Familiarity with service mesh technologies (e.g., Istio) and advanced traffic management patterns.
• Exposure to stream processing frameworks (e.g., Apache Flink) and CDC tools such as Debezium.
• Experience supporting MLOps or AI infrastructure, including ML pipelines, model deployment, or