Principal Architect Automation

• Act as a trusted technical advisor to senior leadership, influencing the architecture and development of applications, platforms, APIs, network automation services, information security capabilities, data systems, operating environments, and cloud-native technologies for highly complex business and technical needs across multiple organizations.

• Act as a trusted technical advisor to senior leadership, influencing the architecture and development of applications, platforms, APIs, network automation services, information security capabilities, data systems, operating environments, and cloud-native technologies for highly complex business and technical needs across multiple organizations.
• Lead the strategy and resolution of highly complex and unique engineering challenges requiring evaluation across multiple technology domains, delivering solutions that are long-term, large-scale, secure, resilient, and maintainable.
• Design and develop production-grade software platforms, APIs, microservices, SDKs, libraries, automation frameworks, and reusable components that enable network and infrastructure engineering capabilities across the enterprise.
• Define software architectures for distributed, event-driven, and asynchronous systems, including service boundaries, data contracts, workflow states, integration patterns, failure handling, concurrency, consistency, and recovery models.
• Establish software engineering standards for application structure, API design, code quality, automated testing, secure development, dependency management, versioning, release engineering, observability, and production readiness.
• Develop reference implementations and contribute directly to high-value or technically complex portions of the platform, particularly where new patterns, technologies, or engineering standards must be proven.
• Lead technical design reviews, architecture reviews, code reviews, failure-mode analysis, and production-readiness assessments for critical platform capabilities.

• Build and evolve internal engineering platforms that provide self-service automation, standardized workflows, reusable services, governed execution paths, and consistent developer experiences.

• Build and evolve internal engineering platforms that provide self-service automation, standardized workflows, reusable services, governed execution paths, and consistent developer experiences.
• Treat shared engineering platforms as products, with clearly defined users, service contracts, roadmaps, adoption measures, documentation, support models, and reliability objectives.
• Create paved roads and golden paths that enable engineering teams to develop, test, certify, release, and operate automation through approved patterns rather than one-off implementations.
• Improve developer productivity through reusable APIs, templates, software development kits, CI/CD pipelines, test harnesses, local development environments, documentation, and automated onboarding.
• Reduce duplicated engineering effort and operational toil by converting common functions into reusable platform services, shared libraries, automation modules, and supported integration patterns.
• Establish appropriate boundaries among platform ownership, application ownership, production execution, operational support, and risk decision-making.

• Design durable workflows for long-running, failure-prone, approval-dependent infrastructure and network processes using Temporal, Celery, or comparable workflow and asynchronous execution technologies.

• Design durable workflows for long-running, failure-prone, approval-dependent infrastructure and network processes using Temporal, Celery, or comparable workflow and asynchronous execution technologies.
• Define patterns for workflows, activities, workers, task queues, events, signals, timers, retries, timeouts, compensating actions, versioning, idempotency, replay safety, and human approval gates.
• Build workflow capabilities that preserve state across failures, support controlled resumption, provide complete execution history, and maintain alignment among technical validation, business approval, and production execution.
• Create reusable workflow components for intake, validation, certification, release approval, change alignment, deployment, verification, rollback, evidence collection, exception handling, and closeout.
• Define clear execution boundaries among orchestration services, CI/CD platforms, approval systems, source-of-truth platforms, AI advisory services, and automation execution engines.

• Design and implement RESTful, event-driven, streaming, and standards-based integrations among enterprise platforms, network infrastructure, source-of-truth systems, workflow engines, observability services, artifact repositories, and change-management systems.

• Design and implement RESTful, event-driven, streaming, and standards-based integrations among enterprise platforms, network infrastructure, source-of-truth systems, workflow engines, observability services, artifact repositories, and change-management systems.
• Define stable, versioned service contracts and data models that allow platform components to evolve independently while maintaining compatibility, security, and traceability.
• Build integrations using technologies and protocols such as REST, RESTCONF, NETCONF, gRPC, webhooks, message queues, event streams, OpenAPI specifications, and structured data formats.
• Develop data pipelines and services that collect, validate, normalize, correlate, and expose network state, software lifecycle data, workflow execution data, telemetry, release evidence, and operational outcomes.
• Establish patterns for data quality, lineage, ownership, freshness, access control, retention, reconciliation, and authoritative-source designation.
• Integrate network source-of-truth platforms such as Nautobot or NetBox with automation services, workflow orchestration, intended-state models, actual-state telemetry, compliance checks, and drift-detection processes.

• Design, build, and operate containerized services using Docker, Kubernetes, OpenShift, Helm, and comparable cloud-native technologies.

• Design, build, and operate containerized services using Docker, Kubernetes, OpenShift, Helm, and comparable cloud-native technologies.
• Develop CI/CD and GitOps capabilities that automate build, testing, security validation, policy enforcement, artifact promotion, environment deployment, and release verification.
• Establish engineering patterns for promoting software and configuration safely across development, test, UAT, and production environments.
• Implement Infrastructure as Code and configuration automation using technologies such as Terraform, OpenTofu, Ansible, Helm, and Kubernetes manifests.
• Define standards for source control, branching, pull requests, protected branches, release tags, artifact integrity, dependency controls, and environment-specific configuration.
• Build automated test capabilities across unit, component, contract, integration, regression, performance, resiliency, and end-to-end testing.

• Design highly available, fault-tolerant, scalable systems that can support mission-critical engineering and infrastructure workflows.

• Design highly available, fault-tolerant, scalable systems that can support mission-critical engineering and infrastructure workflows.
• Establish service-level indicators, service-level objectives, error budgets, availability targets, capacity expectations, and production-readiness requirements for platform services.
• Design observability architectures using metrics, logs, traces, events, flow data, streaming telemetry, and business-level workflow indicators.
• Implement dashboards, alerts, service health indicators, dependency views, and diagnostic capabilities using technologies such as OpenTelemetry, Prometheus, Grafana, ELK, Splunk, and distributed tracing platforms.
• Lead the development of automated detection, diagnostics, remediation, rollback, and evidence-capture patterns.
• Apply failure-mode analysis, resilience testing, controlled fault injection, capacity testing, and performance engineering to identify risks before production adoption.
• Ensure platform teams receive actionable health information rather than relying solely on infrastructure alerts or manual investigation.

• Design and develop governed AI-assisted engineering services that improve knowledge discovery, software development, test generation, release analysis, operational diagnostics, evidence summarization, and engineering decision support.

• Design and develop governed AI-assisted engineering services that improve knowledge discovery, software development, test generation, release analysis, operational diagnostics, evidence summarization, and engineering decision support.
• Build AI application capabilities using retrieval-augmented generation, semantic search, structured outputs, tool integration, model evaluation, and human-in-the-loop approval patterns.
• Develop knowledge services that use approved and authoritative engineering sources, including standards, documentation, software repositories, release evidence, test results, network state, telemetry, and operational history.
• Establish clear boundaries between AI recommendation and production execution. AI-generated recommendations must remain explainable, traceable, reviewable, and subject to approved policy or human controls.
• Define evaluation methods for source grounding, retrieval quality, response accuracy, unsupported claims, recommendation usefulness, and operational risk.
• Partner with security, data, risk, legal, compliance, and AI governance teams to ensure AI-enabled capabilities meet enterprise requirements.

• Embed security, resiliency, auditability, and policy enforcement into software architecture and platform design rather than treating them as post-development activities.

• Embed security, resiliency, auditability, and policy enforcement into software architecture and platform design rather than treating them as post-development activities.
• Design identity, authorization, secrets management, service account, certificate, data protection, and least-privilege patterns for platform services and integrations.
• Establish guardrails that prevent unauthorized execution, bypass of required approvals, unverified artifact promotion, uncontrolled configuration changes, or use of untrusted data.
• Ensure platform actions and workflow transitions produce sufficient evidence for troubleshooting, compliance, risk review, and audit.
• Partner with Information Security and Risk organizations to translate policies and standards into enforceable software controls and measurable engineering requirements.
• Identify technical risks proactively and lead the design and implementation of appropriate mitigation strategies.

• Translate leadership vision, business priorities, and enterprise technology objectives into executable software strategies, architectural roadmaps, platform capabilities, and engineering initiatives.

• Translate leadership vision, business priorities, and enterprise technology objectives into executable software strategies, architectural roadmaps, platform capabilities, and engineering initiatives.
• Provide vision, direction, and technical expertise to leadership on major engineering investments, modernization opportunities, platform decisions, and emerging technologies.
• Influence engineering direction across multiple teams and organizations without relying on direct management authority.
• Define reusable architecture patterns and drive consistent implementation across software, infrastructure, network, reliability, and automation teams.
• Mentor senior engineers, technical leads, architects, and emerging principal engineers through design collaboration, code review, technical forums, and structured knowledge sharing.
• Maintain knowledge of industry engineering practices, software frameworks, cloud-native platforms, AI technologies, observability methods, and network automation developments.
• Evaluate new technologies through prototypes and evidence-based assessments, then define adoption guidance, implementation patterns, and production-readiness criteria.
• Promote a culture of engineering ownership, automation, measurement, experimentation, documentation, and continuous improvement.

• 7+ years of Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
• 7+ years of software engineering, platform engineering, cloud engineering, infrastructure engineering, network automation, SRE, NRE, or equivalent experience demonstrated through one or a combination of work experience, training, military experience, or education.
• 5+ years of experience designing, developing, testing, and supporting production-grade software, distributed systems, automation platforms, or enterprise integration services.
• 3 plus years of Advanced software development experience using Python and at least one additional modern programming language such as Go, Java, C#, .NET, or TypeScript.
• 3+ years experience designing and developing APIs, microservices, asynchronous services, event-driven systems, workflow services, or comparable distributed software components.
• 3 plus years of experience developing software using Git-based workflows, pull requests, peer review, versioning, package management, CI/C