2 months ago
Responsibilities
- Own reliability, availability, performance, compliance, and operational accountability for AI, LLM, and ML-enabled payer platforms.
- Lead operational governance, continuous improvement, incident management, root cause analysis, problem management, and service restoration.
- Oversee MLOps processes including model deployment, monitoring, retraining, validation, and rollback.
- Deploy and manage containerized services with Kubernetes and provision cloud and on-premises infrastructure with Terraform.
- Standardize configuration management using Ansible and support Node.js services and APIs integrating AI and machine learning capabilities.
- Establish Git-based version control, release management, code review, and repository governance practices.
- Drive Linux security hardening, patch management, performance optimization, monitoring, and production triage.
- Guide Python development for data pipelines, AI orchestration, automation frameworks, and analytics workloads.
- Monitor platform health using metrics, logs, traces, Prometheus, Grafana, and other observability tools.
- Collaborate with stakeholders, product owners, platform engineers, AI specialists, operations teams, and healthcare-domain experts.
Requirements
- Strong experience with Kubernetes and Google Cloud Platform, including Google Kubernetes Engine (GKE).
- Strong experience with Terraform, Helm, and GitHub Actions for infrastructure and delivery automation.
- Proficiency in Python, Ansible, and Node.js.
- Strong experience with Prometheus and Grafana observability tooling.
- Solid understanding of Linux systems and networking fundamentals.
- Experience with incident management, on-call support, production triage, automation, and CI/CD pipelines.
- Strong understanding of AI/ML concepts and AIOps practices, including model lifecycle management, monitoring, or AI-driven alerting.
- Google Cloud Architect Certification and Certified Kubernetes Administrator certification are helpful.
- Experience with Java/J2EE, Spring Boot, ML/AI platforms or pipelines, AIOps tools, anomaly detection, predictive analytics, distributed systems, microservices, GPU workloads, Kubeflow, Vertex AI, ML pipelines, or AI-driven monitoring automation is helpful.
Benefits
- Medical, dental, vision, and life insurance, subject to eligibility.
- Paid holidays and paid time off.
- 401(k) plan and contributions.
- Long-term and short-term disability coverage.
- Paid parental leave.
- Employee Stock Purchase Plan.
- Hybrid work arrangement requiring attendance at a client or Cognizant office based on project needs.
Tech Stack
AnsibleGitHub ActionsGoogle Cloud PlatformGrafanaHelmJavaKubernetesLinuxNode.jsPrometheusPythonSpring BootTerraform
Categories
DevOpsSite Reliability
About Cognizant
Cognizant is a public IT services and consulting firm that designs, builds, and runs enterprise technology, including digital engineering, cloud modernization, data/AI, and managed services. It sells consulting, systems integration, and outsourcing on multi-year engagements to large enterprises in healthcare, banking, retail, communications, and manufacturing. Founded in 1994 and headquartered in Teaneck, New Jersey, Cognizant is NASDAQ-listed (CTSH) and a Fortune 500 company.