EverOps

DevOps Engineer

• Support engineering teams by triaging and resolving infrastructure and CI/CD issues
• Build automation that eliminates repetitive operational work
• Contribute to cloud infrastructure improvements across development, staging, and production environments
• Continuously enhance deployment pipelines, monitoring, and infrastructure reliability
• Respond to infrastructure and CI/CD-related tickets from engineering teams
• Troubleshoot build failures, deployment issues, IAM/permissions problems, networking misconfigurations, and container runtime errors
• Assist with debugging issues across cloud environments (dev, staging, prod)
• Identify recurring issues and propose automation-based solutions
• Contribute to Infrastructure as Code (IaC) using tools like Terraform, Pulumi, or CloudFormation
• Improve reusable modules and promote DRY infrastructure patterns
• Write automation scripts and tooling in Python, Go, or Bash to eliminate manual processes
• Help automate environment provisioning, account setup, and deployment workflows
• Support and enhance CI/CD pipelines using GitHub Actions, GitLab CI, Jenkins, or similar tools
• Improve deployment reliability and rollback processes
• Contribute to GitOps-based workflows using tools like ArgoCD or Flux
• Support containerized workloads running on Kubernetes (EKS, AKS, GKE, or self-managed clusters)
• Assist with namespace configuration, RBAC, and cluster hygiene
• Help manage cloud resources across AWS and other major cloud providers
• Participate in environment setup and account structuring projects
• Assist in implementing and tuning monitoring and alerting systems (Datadog, Cloud-native monitoring tools)
• Help reduce alert fatigue through automation and improved signal quality
• Contribute to post-incident reviews and identify preventative automation opportunities
• Document processes and create internal runbooks
• Identify opportunities to automate manual support workflows
• Collaborate with senior engineers on platform enhancement projects

Backend Engineer

• Lead technical workshops to identify, refine, and prioritize high-impact AI and GenAI use cases aligned with business objectives.
• Translate business problems into system design requirements and AI workflows.
• Assess existing data platforms, pipelines, governance, and accessibility for AI workloads.
• Evaluate data quality, lineage, security, and suitability for training, RAG, and inference patterns.
• Design AI architectures that comply with enterprise security, privacy, and regulatory constraints (PII, PHI, internal policies).
• Evaluate and design integrations across APIs, event streams, and existing systems.
• Evaluate and recommend foundation models and AI services, including Amazon Bedrock, Amazon Nova, and open-source models.
• Analyze tradeoffs across cost, latency, accuracy, and scalability.
• Design GenAI patterns such as RAG, agent workflows, and inference pipelines.
• Produce high-level and detailed AWS reference architectures for prioritized AI use cases.
• Define phased implementation roadmaps that balance speed, risk, and long-term maintainability.
• Identify PoC scope that can be executed within a short engagement.
• Partner with stakeholders to develop ROI and TCO models for AI initiatives.
• Provide cost modeling for model usage, data pipelines, infrastructure, and operations.
• Deliver AI assessment findings and recommendations.
• Create target-state AI platform architecture diagrams.
• Summarize data readiness and compliance assessments.
• Provide model evaluation and selection rationale.
• Define phased implementation roadmap.
• Design and validate PoC.
• Prepare executive-ready presentations and documentation.

DevOps Engineer

AI Platform Architect