Senior Platform Engineer, DevSecOps and Moder

Investment Company Institute.; Washington (District of Columbia); On-site; USD 162,000 - 180,000; Full time

Job summary

Investment Company Institute is seeking a hands-on Senior Platform Engineer to strengthen DevSecOps practices and lead migration to AI-ready, secure, automated infrastructure across hybrid on-premises and cloud environments in Washington, DC.

The role focuses on architecture judgement, automated deployment pipelines, and mentoring outsourced resources to build and maintain scalable IT platforms supporting data and analytics workloads.

Qualifications

• Bachelor’s degree in computer science, information systems, engineering, or related field; master's preferred.

Responsibilities

• Identify and execute safe migration paths to AI-ready platforms.

Skills

DevSecOps

Platform engineering

Terraform

Ansible

Kubernetes

Monitoring

Security

Education

Bachelor’s degree in CS/IS/Engineering

Master’s degree preferred

Tools

Solaris

Linux

Terraform

Ansible

Kubernetes

NGINX

Airflow

n8n

Kafka

MinIO

Keycloak

Grafana

Prometheus

Kibana

Job description

Senior Platform Engineer, DevSecOps and Moder

Full Time Washington, DC, US

Salary Range: $162,000.00 To $180,000.00 Annually

ICI is looking for a hands-on Senior Platform Engineer to support existing infrastructure, strengthen DevSecOps practices, and drive transformation to an AI-ready, secure, automated, event and data-driven target architecture across hybrid on-premises and cloud environments.

Role Summary

The role combines AI-augmented DevSecOps engineering, environment management, secure automation, vendor coordination, platform modernization, and cloud data platform support. It is hands-on and requires architecture judgment plus the ability to train outsourced resources to build, maintain, and support infrastructure.

The engineer will bridge IT infrastructure, development, data, architecture, and AI by designing automated deployment pipelines. The role will apply AI-assisted analysis to migration planning, diagnostics, documentation, and support. It will use infrastructure automation and platform tools to manage consistent microservices and data environments from development through production and BCP.

The role will assist with product selection and big data architecture for public data workloads that support statistical, R-based, and AI-augmented analytics. It will build flexible infrastructure for event-driven architecture, API integration, monitoring, AI-enabled operations, and open-source platform capabilities.

Primary Responsibilities

• Identify and execute safe migration paths from legacy platforms to modern, scalable, efficient, AI-ready platforms.
• Support and secure application infrastructure across development, SIT, QA, UAT, production, and BCP environments.
• Design secure deployment pipelines with automated controls, AI-assisted quality checks, governance gates, release controls, and audit evidence.
• Use Terraform, Ansible, Kubernetes, and related tools to provision, configure, orchestrate, and operate consistent environments.
• Create operating frameworks for Kubernetes, Tomcat, Airflow, Kafka, MinIO, Keycloak, n8n, Kong, NGINX, and related services.
• Implement observability using Grafana, Prometheus, Loki, OpenSearch, Kibana, Graylog, Wazuh, or equivalent monitoring and log platforms.

Platform Engineering and Operations

• Maintain, harden, and standardize platform infrastructure across hybrid on-premises and cloud environments.
• Support legacy Solaris environments during transition, stabilization, migration, and decommissioning.
• Create AI-augmented policies, runbooks, training resources, and operating standards for internal and outsourced infrastructure teams.
• Define backup, recovery, handoff, access, incident response, triage, and operational control procedures.
• Partner with outsourced infrastructure, development, security, data, and architecture teams to reduce risk and move toward the target architecture.

Platform and Legacy System Support

• Support Sybase administration on Solaris and Linux with data engineering and application teams.
• Analyze data flows, reports, interfaces, batch jobs, integrations, and dependencies using AI-assisted discovery where appropriate.
• Determine what belongs in the data and reporting platform versus full-stack applications or API-enabled services.
• Document technical debt, risks, dependency maps, migrationgrade considerations, and event-driven and AI-augmented data flow opportunities.
• Contribute to public data workload architecture, including product selection, ingestion, storage, processing, access controls, analytics, and integration.

Required Experience and Qualifications

• Bachelor’s degree in computer science, information systems, engineering, or related field required; master’s preferred.
• 15+ years of hands-on experience in DevSecOps, platform engineering, systems engineering, or equivalent roles.
• 10+ years of system architecture experience for secure, scalable, resilient, production-grade systems.
• Experience with Solaris and Linux administration, automation, scripting, pipelines, environment management, monitoring, and troubleshooting.
• Knowledge of Sybase administration on Solaris and Linux, legacy platforms, release governance, and environment promotion controls.
• Experience with Terraform, Ansible, Kubernetes, containerization, orchestration, secrets management, automated recovery, and platform tools.
• Experience with Kubernetes, NGINX, Airflow, n8n, Kong, Kafka, MinIO, Keycloak, and related open-source services.
• Experience with observability tools such as Grafana, Prometheus, Kibana, or equivalent platforms.
• Familiarity with cloud data platforms, big data architecture, and R-based tools such as Stata and Positron.
• Experience using AI-assisted engineering for migration analysis, configuration review, documentation, troubleshooting, knowledge management, and training.
• Ability to diagnose platform, application, data flow, authentication, integration, and environment issues across legacy and modern stacks.
• Hands-on execution with architecture judgment to turn target-state direction into working, AI-ready infrastructure.
• Strong judgment balancing modernization speed, security, stability, cost, vendor dependency, and operational risk.
• Ability to bridge infrastructure, development, architecture, security, data, AI, and outsourced resources while owning outcomes.
• Clear communication of risks, tradeoffs, dependencies, procedures, and escalation paths.
• Bias toward AI augmentation, automation, standardization, observability, repeatability, and reducing manual work.