Job description

Description: Local candidates preferred. The Principal Engineer provides technical leadership for the architecture, integration, governance, implementation, and operational support of enterprise AI engineering platforms. This role drives AI-enabled transformation across the Software Development Lifecycle (SDLC), including requirements management, design, development, testing, deployment, release management, and operations. The position is responsible for designing and implementing scalable, secure, resilient, and compliant AI engineering solutions that integrate with enterprise development platforms, DevSecOps toolchains, cloud and on-premises infrastructure, observability solutions, and operational support processes. The Principal Engineer partners with engineering, architecture, infrastructure, security, risk, compliance, and product teams to establish enterprise standards, reusable patterns, and best practices for AI-enabled software engineering capabilities. Key Responsibilities Technical Leadership and Strategy Serve as a technical advisor on complex AI engineering, platform, application, infrastructure, security, governance, and software delivery initiatives. Lead technical strategy and solution development for enterprise-scale engineering and platform challenges. Translate business requirements, technology strategy, risk considerations, and emerging AI capabilities into scalable engineering solutions. Provide technical leadership and guidance for enterprise AI enablement initiatives. Evaluate industry trends, tools, and technologies and recommend solutions that improve engineering productivity, delivery quality, operational effectiveness, and business outcomes. Influence architecture standards, engineering practices, integration patterns, and technology roadmaps across multiple teams. AI Engineering Platform Architecture Lead the architecture and evolution of enterprise AI engineering platforms across SaaS, cloud, desktop, and on-premises environments. Design scalable, resilient, secure, and supportable integration patterns for AI coding assistants, software engineering agents, and developer productivity platforms. Define enterprise architecture patterns for Model Context Protocol (MCP), MCP gateways, APIs, enterprise services, and platform interoperability. Provide hands-on leadership for solution design, implementation, integration, testing, deployment, and operationalization. Establish reusable architecture patterns, engineering standards, reference architectures, implementation guidance, and platform controls. Lead architecture design activities involving: API architectures Authentication and authorization Network connectivity Service integration Platform security Scalability and resiliency AI-Enabled Software Development Lifecycle Lead integration of AI capabilities throughout the SDLC, including requirements, design, development, testing, deployment, release, and operations. Enable integration with requirements management and collaboration platforms. Support AI-enabled design workflows and architecture tooling. Integrate AI capabilities with developer platforms, source control systems, software supply chain tools, CI/CD platforms, and DevSecOps solutions. Enable integration with testing, validation, quality engineering, deployment, and release management platforms. Support integration with observability, monitoring, logging, and operational management platforms. Delivery, Security, Risk, and Governance Lead initiatives from requirements through architecture, implementation, validation, production readiness, deployment, and operational support. Define functional and non-functional requirements and conduct platform capability assessments. Guide implementation of identity, access management, secrets management, gateway integration, network connectivity, and security controls. Lead functional, integration, security, performance, and user acceptance testing activities. Partner with security, risk, compliance, legal, procurement, and governance stakeholders as required. Ensure solutions align with enterprise security, technology risk, data protection, regulatory, and operational requirements. Support architecture reviews, risk assessments, security reviews, and governance approval processes. Production Readiness and Operations Lead production readiness planning and deployment activities. Establish standards for monitoring, logging, alerting, incident management, and operational support. Develop operational procedures, implementation plans, rollback strategies, and support models. Support production deployments and post-implementation validation activities. Partner with DevOps, Platform Engineering, Release Engineering, and Site Reliability Engineering teams to improve reliability, resiliency, performance, and supportability. Promote continuous improvement through operational metrics, feedback mechanisms, and process optimization. Cross-Functional Collaboration Partner with product owners, developers, architects, designers, quality engineers, platform engineers, release teams, and site reliability engineers. Collaborate with architecture, cybersecurity, infrastructure, risk, compliance, and operations teams. Facilitate technical discussions and obtain alignment across stakeholders. Communicate technical concepts, risks, trade-offs, and recommendations to technical and non-technical audiences. Mentor engineers and architects and contribute to engineering communities of practice. Required Qualifications Bachelor's degree in Computer Science, Engineering, Information Systems, or a related field; or an equivalent combination of education, training, military experience, and relevant work experience. Minimum of 15 years of experience in software engineering, platform engineering, systems engineering, infrastructure engineering, architecture, or related technical disciplines. Minimum of 5 years of experience leading enterprise-scale engineering initiatives, architecture programs, platform integrations, or technology transformation efforts. Minimum of 5 years of hands-on experience in one or more of the following: Software Engineering Platform Engineering DevOps Engineering Cloud Engineering Infrastructure Automation API Integration Experience designing and implementing enterprise-scale solutions within SDLC, DevSecOps, CI/CD, cloud, SaaS, or on-premises environments. Experience with: Enterprise architecture principles API and service integration Authentication and authorization Secrets management Network connectivity Security controls Experience leading cross-functional technology initiatives involving engineering, architecture, security, risk, compliance, and operations stakeholders. Experience establishing production readiness, observability, operational support, governance, and continuous improvement practices. Demonstrated ability to communicate technical information effectively to diverse stakeholder groups. Preferred Qualifications Experience with Generative AI, Agentic AI, Large Language Models (LLMs), AI engineering platforms, or AI-assisted software development solutions. Knowledge of: Model Context Protocol (MCP) MCP gateways Agent orchestration frameworks Enterprise AI architecture patterns Experience integrating AI-enabled developer productivity tools, coding assistants, or software engineering agents into enterprise environments. Experience with one or more of the following platforms or comparable technologies: GitHub Enterprise GitHub Actions Jira Confluence Figma Artifactory Sonar Checkmarx Black Duck BrowserStack BlazeMeter ReportPortal Harness ServiceNow Splunk AppDynamics Ansible Experience in regulated industries such as financial services, healthcare, insurance, telecommunications, utilities, or similar environments. Knowledge of secure architecture, third-party risk management, SaaS governance, data protection, technology risk management, compliance, and operational controls. Experience creating reusable frameworks, reference implementations, architecture standards, technical guidance, adoption plans, and enablement programs. Demonstrated ability to influence technical strategy and architecture decisions across multiple organizations. Strong written, verbal, presentation, stakeholder management, and collaboration skills.

Requirements

Must-haves

Technical Leadership and Strategy
Serve as a technical advisor on complex AI engineering, platform, application, infrastructure, security, governance, and software delivery initiatives.
Lead technical strategy and solution development for enterprise-scale engineering and platform challenges.
Translate business requirements, technology strategy, risk considerations, and emerging AI capabilities into scalable engineering solutions.
Provide technical leadership and guidance for enterprise AI enablement initiatives.
Evaluate industry trends, tools, and technologies and recommend solutions that improve engineering productivity, delivery quality, operational effectiveness, and business outcomes.
Influence architecture standards, engineering practices, integration patterns, and technology roadmaps across multiple teams.
AI Engineering Platform Architecture
Lead the architecture and evolution of enterprise AI engineering platforms across SaaS, cloud, desktop, and on-premises environments.
Design scalable, resilient, secure, and supportable integration patterns for AI coding assistants, software engineering agents, and developer productivity platforms.
Define enterprise architecture patterns for Model Context Protocol (MCP), MCP gateways, APIs, enterprise services, and platform interoperability.
Provide hands-on leadership for solution design, implementation, integration, testing, deployment, and operationalization.
Establish reusable architecture patterns, engineering standards, reference architectures, implementation guidance, and platform controls.
API architectures
Authentication and authorization
Network connectivity
Service integration
Platform security
Scalability and resiliency
AI-Enabled Software Development Lifecycle
Lead integration of AI capabilities throughout the SDLC, including requirements, design, development, testing, deployment, release, and operations.
Enable integration with requirements management and collaboration platforms.

Nice-to-haves

Local candidates preferred.
Experience with Generative AI, Agentic AI, Large Language Models (LLMs), AI engineering platforms, or AI-assisted software development solutions.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aiapici/cdjirafigma

Platform: jobdiva
Effort: medium