• Design, operate, and scale GPU and CPU clusters for ML training and inference (Slurm, Kubernetes, autoscaling, queueing, quota management). • Automate infrastructure provisioning and configuration using infrastructure‑as‑code (Terraform, CloudFormation, cluster‑tooling) and configuration management. • Build and maintain robust ML pipelines (data ingestion, training, evaluation, deployment) with strong guarantees around reproducibility, traceability, and rollback. • Implement and evolve ML‑centric CI/CD: testing, packaging, deployment of models and services. • Own monitoring, logging, and alerting across training and serving: GPU/CPU utilization, latency, throughput, failures, and data/model drift (Grafana, Prometheus, Loki, CloudWatch). • Work with terabyte‑scale datasets and the associated storage, networking, and performance challenges. • Partner closely with ML engineers and researchers to productionize their work, translating experimental setups into robust, scalable systems. • Participate in on‑call rotation for critical ML infrastructure and lead incident response and post‑mortems when things break.

• Design, implement, and manage CI/CD pipelines using GitHub Actions, GitOps, GCP-native tools, Azure DevOps, and Jenkins. • Develop, automate, and maintain infrastructure as code across multi-cloud environments (GCP, Azure; Kubernetes, Cloud Run, AKS, GKE). • Build, deploy, and operate containerized services, ensuring health, security, scalability, and high availability. • Integrate tools, platforms, and APIs to streamline operations across AI/ML and virtual agent ecosystems. • Develop and maintain automation scripts and tooling using Python, Shell, and Java/Groovy where appropriate. • Monitor system and application health, implement proactive alerting, and continuously improve reliability and performance. • Troubleshoot infrastructure, networking, and deployment issues across cloud services and application stacks. • Collaborate with engineering and product teams to optimize release processes and platform reliability. • Promote DevOps best practices and maintain clear documentation for system architecture, operations, and troubleshooting.

• Design, implement, and maintain the organization’s security architecture in alignment with federal security standards (e.g., FISMA, NIST SP 800-53, 800-171) and contract requirements • Lead security planning and risk assessments for government systems hosted in AWS • Serve as the primary security point of contact for government programs, overseeing incident response, vulnerability management, and system hardening activities • Develop and maintain security documentation required for system authorization, including System Security Plans (SSPs), Plans of Action and Milestones (POA&Ms), Security Assessment Reports (SARs), and Continuous Monitoring strategies • Support the Authority to Operate (ATO) process across multiple projects, working closely with compliance teams, federal partners, and internal stakeholders • Architect, oversee and support implementation of security controls across AWS services (e.g., IAM, KMS, Security Hub, GuardDuty, CloudTrail, Config, WAF, etc.) • Perform regular audits, security assessments, and continuous monitoring to ensure compliance with government standards and internal policies • Collaborate with engineering teams to integrate security into SDLC/DevOps pipelines, using tools such as SonarQube, Snyk, Tenable, and Jenkins • Lead incident response efforts for government systems, including containment, eradication, and recovery, while maintaining proper documentation and communication protocols • Research and recommend emerging AWS security services and technologies to improve security posture and maintain compliance • Mentor junior DevSecOps team members and foster a culture of security-first thinking across the organization • Interface with federal agency stakeholders, auditors, and security assessors to represent the organization’s security practices and compliance efforts • Participate in proposal development and pre-award planning by advising on security architecture and compliance strategies for new federal opportunities

• Design and architect highly available and scalable OpenShift/Kubernetes infrastructure for banking applications on on-premise servers • Lead and implement comprehensive monitoring and observability strategy using Prometheus and Grafana • Design and oversee centralized logging infrastructure using ELK Stack (Elasticsearch, Logstash, Kibana) • Lead SRE best practices implementation and adoption of production support standards across teams • Mentor and coach junior SRE and DevOps engineers on OpenShift, Kubernetes, monitoring, and production support • Define and implement Service Level Indicators (SLIs), Objectives (SLOs), and Agreements (SLAs) with measurable metrics • Lead incident response strategy, post-incident reviews, and drive continuous improvement in production stability • Architect and implement advanced alerting, monitoring dashboards, and visualization strategies using Prometheus and Grafana • Design automation frameworks and tools to reduce operational toil and improve production efficiency • Lead OpenShift/Kubernetes cluster upgrades, security patches, and infrastructure modernization on-premise • Establish production support procedures, on-call rotation policies, and escalation frameworks • Optimize system performance, cost, and resource utilization across containerized on-premise infrastructure • Conduct capacity planning, performance optimization, and infrastructure scaling initiatives • Lead technical architecture reviews and infrastructure design decisions for banking applications • Manage on-premise data center resources and infrastructure planning • Participate in 24/7 on-call rotation and escalation for critical production incidents • Ensure compliance, security hardening, and disaster recovery procedures for financial systems