AWS DevOps / SRE Engineer Datadog & AIOps

• We are seeking a hands-on AWS DevOps Engineer with strong Site Reliability Engineering capabilities and deep Datadog experience. This role will design and improve secure, scalable CI/CD pipelines; increase platform reliability through observability, automation, and SLO-driven practices; and introduce practical AIOps and generative AI capabilities that improve build quality, deployment safety, incident response, and engineering productivity.
• Day-to-Day Job Duties
• Design, build, and maintain resilient AWS environments using services such as EKS, EC2, S3, IAM, Lambda, RDS, CloudWatch, Route 53, ALB/NLB, and Secrets Manager.
• Build, standardize, and optimize CI/CD pipelines using GitLab CI, GitHub Actions, Jenkins, or similar platforms, with automated testing, quality gates, approvals, rollback, and progressive-delivery controls.
• Apply SRE practices by defining service-level indicators, service-level objectives, error budgets, availability targets, and operational-readiness criteria.
• Implement and administer Datadog capabilities including infrastructure monitoring, APM, log management, Real User Monitoring, synthetics, dashboards, monitors, service maps, and incident workflows.
• Create actionable observability and alerting strategies that reduce noise, improve mean time to detect and recover, and support rapid root-cause analysis.
• Automate infrastructure provisioning and configuration using Terraform, CloudFormation, Ansible, or equivalent Infrastructure as Code tools.
• Operate containerized workloads using Docker and Kubernetes/EKS, including autoscaling, health checks, resource optimization, and cluster reliability.
• Integrate security and compliance controls into CI/CD, including secrets management, IAM least privilege, vulnerability scanning, SAST/DAST, dependency checks, artifact integrity, and audit evidence.
• Use AIOps and generative AI to improve pipeline efficiency through intelligent failure analysis, configuration review, test generation, anomaly detection, change-risk scoring, and remediation recommendations.
• Develop automation and operational tooling using Python, Bash, PowerShell, or similar scripting languages.
• Lead production troubleshooting, incident response, post-incident reviews, problem management, and permanent corrective-action tracking.
• Partner with application, platform, security, QA, and product teams to improve deployment frequency, change-failure rate, lead time, reliability, and recovery performance.
• Maintain runbooks, architecture diagrams, operational procedures, and engineering standards in Confluence, Jira, or similar tools.
• Provide technical guidance and mentor engineers on cloud reliability, observability, automation, DevOps, and SRE practices.

• A hands-on engineer who balances delivery velocity with production reliability and operational discipline.
• A proactive problem-solver who uses data, automation, and observability to prevent recurring issues.
• A collaborative technical leader who can influence teams to adopt secure cloud, DevOps, SRE, and AI-assisted engineering practices.
• Someone comfortable owning high-visibility production platforms and driving improvements from design through operations., * Minimum 5+ years of experience in DevOps, Cloud Engineering, Platform Engineering, or a related role supporting enterprise production systems.
• Minimum 3+ years of hands-on experience designing, deploying, and operating solutions on AWS.
• Strong experience building and supporting production-grade CI/CD pipelines; GitLab CI experience is preferred.
• Demonstrated SRE experience with SLOs/SLIs, error budgets, incident response, on-call operations, reliability engineering, capacity planning, and blameless post-incident reviews.
• Strong hands-on Datadog experience across metrics, logs, APM/tracing, dashboards, alerting, monitors, synthetics, integrations, and service-level reporting.
• Experience with Terraform or CloudFormation and repeatable Infrastructure as Code practices.
• Experience with Docker and Kubernetes; AWS EKS experience is strongly preferred.
• Proficiency in at least one scripting or programming language such as Python, Bash, PowerShell, Go, or JavaScript/TypeScript.
• Strong understanding of Linux, networking, IAM, secrets management, cloud security, and production troubleshooting.
• Experience working in Agile environments and using tools such as Jira, Confluence, and Git.
• Strong communication, collaboration, documentation, and problem-solving skills with a high degree of ownership.

Preferred Qualifications:

• Hands-on experience applying AIOps, machine learning, or generative AI to CI/CD, observability, incident management, automated testing, code review, or root-cause analysis.
• Experience integrating LLM-based assistants or agents with developer platforms, repositories, ticketing systems, observability tools, or operational runbooks using secure enterprise controls.
• Experience with progressive delivery and GitOps tools such as Argo CD, Flux, feature flags, canary deployments, and blue/green deployments.
• Experience with OpenTelemetry, Prometheus, Grafana, Splunk, CloudWatch, or other observability platforms in addition to Datadog.
• Knowledge of DORA metrics and experience improving deployment frequency, lead time for changes, change-failure rate, and mean time to recovery.
• Experience supporting regulated, financial-services, or other highly controlled enterprise environments.
• AWS, Kubernetes, Terraform, Datadog, or relevant DevOps/SRE certifications.
• Experience leading technical initiatives or mentoring engineering teams.
• Key Success Measures
• Improved CI/CD speed, stability, reuse, and developer adoption.
• Reduced deployment failures, alert noise, incident recurrence, and recovery time.
• Clear service-health reporting through Datadog dashboards, SLOs, and actionable alerts.
• Increased automation across provisioning, testing, release controls, and operational remediation.
• Safe, measurable adoption of AIOps and AI capabilities without weakening security or governance.