Zelis

Customer Support

• Take ownership of resolving moderately complex client issues
• Use sound judgment to troubleshoot problems and propose solutions
• Proactively identify opportunities to enhance the client experience
• Support initiatives that increase client engagement, satisfaction, and loyalty
• Partner with internal teams to relay client insights and advocate for client needs
• Participate in cross-team projects that improve service delivery
• Identify inefficiencies in client experience workflows
• Create and maintain internal documentation and client-facing FAQs

Client Support Specialist

Director

• Lead quantitative and qualitative research across business units and within marketing to deliver actionable business insights and intelligence.
• Lead an international team of analysts, fostering a collaborative and high-performing culture with responsibility for recruiting, mentoring, and leading a team.
• Provide key frameworks and be the subject matter expert on research methodologies, interpreting survey results, nurture and define best practice statistical techniques, and where necessary reform and refine methods and survey questions while keeping business needs and realities top of mind.
• Lead efforts in competitive analysis, delivering actionable intelligence that informs the Zelis marketing, sales, and product strategies.
• Ensure a culture of openness and willingness to pursue experimentation while establishing efficient processes within the Research & Insights team, including testing and utilizing AI and synthetic research techniques.
• Embrace the art and science of customer and consumer insight generation.

Director, Research and Market Intelligence

Machine Learning Engineer

• Build and maintain monitoring infrastructure for conventional machine learning models, with capabilities for performance tracking, drift detection, and alerting.
• Research, evaluate, and implement monitoring strategies and tools for Generative AI systems, including LLMs and Agentic AI architectures.
• Collaborate with ML Engineers, Data Scientists, and DevOps teams to deploy, manage, and monitor models in production.
• Develop and support scalable, secure, and automated data pipelines using Snowflake, SQL, and Python for training, serving, and monitoring ML and GenAI models.
• Leverage AutoML tools and frameworks (e.g., MLflow, Kubeflow, SageMaker Autopilot) to streamline experimentation and deployment.
• Design dashboards and reporting systems to visualize model health metrics and surface key operational insights.
• Ensure auditability, reproducibility, and compliance for model performance and data flow in production environments, with consideration for regulatory standards like GDPR and HIPAA.
• Maintain CI/CD workflows and version-controlled codebases (e.g., Git) for ML infrastructure and pipelines.
• Utilize containerization and orchestration technologies (e.g., Docker) to manage scalable ML infrastructure.
• Leverage tools such as Streamlit and Python visualization libraries to present insights from model and data monitoring.
• Perform root cause analyses on model degradation or data quality issues, and proactively implement improvements.
• Stay current on industry developments related to ML observability, model governance, responsible GenAI practices, and AI security.
• Contribute to analytics projects and data engineering initiatives as needed.
• Provide off-hours support for critical deployments or urgent data/model issues.

ML Ops Engineer

Backend Engineer

• Architect and operationalize a Kubernetes platform(s) on AWS supporting multi-account, multi-region deployments aligned with AWS Well-Architected principles
• Define platform capabilities including compute autoscaling, pod networking, network policies, load balancing, and storage drivers
• Lead platform roadmap development and cross-functional alignment with architecture, security, FinOps, and product engineering
• Define and own lifecycle management, patching, and performance tuning of worker nodes
• Design and manage autoscaling groups, node pools, and lifecycle automation
• Architect secure and scalable VPCs, subnets, route tables, NAT gateways, and security groups
• Manage cluster-level settings including version upgrades, endpoint access, audit logging, and control plane integrations
• Deploy and maintain cluster add-ons such as CoreDNS, kube-proxy, metrics server, and custom controllers
• Define and enforce RBAC, network policies, pod security standards, and IAM roles for service accounts
• Implement resource quotas, tagging strategies, and budget controls to support chargeback models and cost transparency
• Drive standardization in tooling, automation, patching, and observability across Kubernetes clusters
• Own SLAs, SLOs, incident response playbooks, and platform reliability engineering practices
• Develop templates and automation to empower developers to build and run Kubernetes platform(s)
• Build and maintain reusable service catalog products, CDK with Python, and CI/CD pipelines to support self-service infrastructure provisioning
• Champion developer experience through clear interfaces, documentation, and onboarding support
• Partner with architecture, security, FinOps, DevOps, and product teams to align platform capabilities with business outcomes

Senior Kubernetes Engineer

Account Manager

• Manage and oversee revenue targets, including budgets, forecasts, and product utilization review for the assigned book of business.
• Responsible for client renewals and retention.
• Successfully facilitate contract negotiations with their leader and manage the complete life cycle to renew client agreements.
• Proactively identify and mitigate risks such as volume decreases, service gaps, and changes in client relationships.
• Drive revenue growth through existing product optimization.
• Successfully uncover and qualify opportunities for growth to deliver great client value.
• Conduct and lead all client business reviews and account plans to optimize value within the client's book of business.

• Provide software-related custom integration and product implementation support
• Apply technology and Zelis product knowledge to address client needs
• Learn about the client's technical system, software and current processes
• Communicate regularly with clients for status updates and requirements gathering
• Develop and manage the implementation project plan
• Gather client requirements
• Customize explanations of benefits and checks or ID cards using Zelis processes
• Manage multiple client implementations simultaneously

Client Support Specialist

Director, Research and Market Intelligence

ML Ops Engineer

Senior Kubernetes Engineer

Account Manager

Implementation Support Specialist