At ABB, we help industries run leaner and cleaner—and every person here makes that happen. You’ll be empowered to lead, supported to grow, and proud of the impact we create together. Join us and help run what runs the world.
This Position reports to:
Digital Solution Engineering ManagerWhat we believe in
ABB’s Process Automation business area enables customers to operate some of the world’s largest and most complex industrial infrastructures, helping them outrun – leaner and cleaner. We offer a broad range of automation, electrification and digital solutions for process, hybrid and maritime industries, including industry-specific integrated control and software as well as measurement and analytics solutions and services.
Your role and responsibilitiesIn this role, we are looking for an experienced Principal Architect / Lead Architect – Industrial AI Platforms to join our Industrial Automation Digital Organization. The role requires a highly accomplished technology leader with extensive experience in architecting and governing large-scale enterprise AI platforms and cloud-native solutions. The candidate will be responsible for defining enterprise architecture standards and designing scalable AI ecosystems involving Copilot platforms, Agentic AI frameworks, MCP integrations, LLMOps workflows, and AI orchestration systems. This role demands strong technical leadership to drive engineering excellence, platform governance, innovation, and delivery of secure, resilient, and intelligent Industrial AI solutions.
The work model for the role is: Hybrid
You will be mainly accountable for:
Define enterprise architecture standards, coding guidelines, and platform engineering best practices for next-generation AI systems.
Architect and design scalable enterprise AI platforms, including High-Level Design (HLD) and Low-Level Design (LLD), involving Copilot ecosystems, Agentic AI frameworks, MCP integrations, LLMOps workflows, and AI orchestration systems using Python, .NET, Angular, and cloud-native technologies.
Create architecture-driven work breakdown structures (WBS) for platform capabilities, AI services, orchestration layers, and enterprise integrations.
Ensure cybersecurity, AI governance, secure model access, prompt safety, tenant isolation, and compliance best practices are implemented across the platform landscape.
Guide engineering teams on coding standards, testing frameworks, resiliency engineering, observability practices, and debugging methodologies to achieve engineering excellence.
Conduct architecture reviews and code reviews to ensure scalability, maintainability, performance, and adherence to enterprise engineering standards.
Drive implementation of automated testing strategies, CI/CD pipelines, deployment governance models, and AI platform release management practices.
Identify and resolve performance bottlenecks across AI inference pipelines, orchestration systems, vector retrieval layers, distributed services, and enterprise integrations.
Create and maintain architecture documentation, solution blueprints, AI workflow diagrams, integration models, reference architectures, and platform governance artifacts.
Collaborate with Product Management and Engineering Leadership teams on roadmap planning, MVP realization, prioritization, and long-term platform evolution strategies.
Ensure architecture and engineering documentation remains standardized, accessible, and continuously updated for all stakeholders.
Define multi-tenancy models, scalability approaches, AI observability frameworks, failover mechanisms, caching strategies, asynchronous processing patterns, and resiliency architectures for enterprise AI workloads.
Mentor and guide Technical Leads, Senior Engineers, and Platform Engineering teams on architectural best practices and enterprise engineering principles.
Provide technical leadership and strategic direction for globally distributed engineering teams delivering AI platform capabilities.
Delegate architecture initiatives, review dependencies, and proactively remove technical blockers to improve delivery effectiveness.
Collaborate effectively with Product, Cloud, DevOps, Security, AI/ML, and customer-facing teams to ensure alignment across the organization.
Provide regular architecture governance updates, technical reviews, and engineering progress reports to senior leadership.
Foster a culture of innovation, technical excellence, collaboration, and knowledge sharing across engineering teams.
Drive cross-functional alignment for enterprise AI platform integrations and customer scalability initiatives.
Work closely with Scrum teams to ensure architecture alignment with Agile delivery methodologies.
Support sprint planning, backlog refinement, architecture grooming sessions, and technical risk identification activities.
Encourage continuous improvement in engineering processes, architecture governance frameworks, and overall platform quality.
Identify opportunities to optimize engineering workflows, deployment automation capabilities, and AI delivery pipelines.
Provide mentorship and coaching on scalable AI engineering practices and Agile technical execution.
Bachelor's or Master's degree in Computer Science, Engineering, Artificial Intelligence, or a related technical discipline.
Proven experience of 8+ years in technical leadership, enterprise architecture, and software engineering roles, with demonstrated success leading complex platform initiatives.
Expert knowledge of LLMOps principles, Agentic AI systems, MCP architecture, Natural Language Processing (NLP) frameworks, and distributed AI platform architectures.
Deep understanding of model lifecycle management, orchestration frameworks, monitoring strategies, AI governance practices, and production-scale AI deployments.
Extensive experience with AI observability practices, prompt governance frameworks, vector databases, Retrieval-Augmented Generation (RAG) pipelines, and AI telemetry systems.
Strong hands-on experience with the Microsoft Azure ecosystem, including Azure OpenAI, Azure Kubernetes Service (AKS), Azure Cosmos DB, Azure App Services, Azure SQL, and enterprise cloud networking and security concepts.
Strong expertise in scalable distributed systems, data structures, algorithms, asynchronous programming, and microservices architecture patterns.
Proven experience with Kubernetes, CI/CD pipelines, deployment automation, DevSecOps practices, Infrastructure as Code (IaC), and cloud-native engineering methodologies.
Demonstrated ability to architect secure, scalable, resilient, and highly available web applications and REST API-based systems.
Understanding of Industrial IoT protocols and standards such as MQTT and OPC UA is preferred.
Experience mentoring technical teams, driving engineering governance, and building high-performing engineering organizations.
Strong communication, technical documentation, stakeholder management, and cross-functional collaboration skills.
ABB is a leading global technology company that energizes the transformation of society and industry to achieve a more productive, sustainable future. With a history of excellence stretching back more than 130 years, ABB has been a pioneer through the four Industrial Revolutions and is at the forefront of Industry 4.0. By delivering digitalization across its electrification, robotics, automation, and motion portfolio, ABB pushes the boundaries of technology to drive performance to new levels.
Take your next career step at ABB with a global team that is energizing the transformation of society and industry to achieve a more productive, sustainable future.
At ABB, we have the clear goal of driving diversity and inclusion across all dimensions: gender, LGBTQ+, abilities, ethnicity, and generations. Together, we are embarking on a journey where each and every one of us, individually and collectively, welcomes and celebrates individual differences.
Join our dynamic team and contribute to the development of cutting-edge Industrial AI, Copilot platforms, Agentic AI systems, and cloud-native digital solutions that shape the future of intelligent industrial operations.
Employment may be subject to applicable background checks and pre-employment screening as per company policy.
Building a cleaner, smarter future takes all kinds of minds: the curious, the courageous, and the creative. We welcome people from all backgrounds and experiences.
Ready to make an impact? Apply today or visit www.abb.com to learn more about the impact of our solutions across the globe.
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Skills Required
- Bachelor's or Master's degree in Computer Science, Engineering, Artificial Intelligence, or related technical discipline
- Proven experience of 8+ years in technical leadership, enterprise architecture, and software engineering roles
- Expert knowledge of LLMOps principles, Agentic AI systems, MCP architecture, and distributed AI platform architectures
- Deep understanding of model lifecycle management, orchestration frameworks, monitoring strategies, and production-scale AI deployments
- Experience with AI observability, prompt governance, vector databases, and Retrieval-Augmented Generation (RAG) pipelines
- Strong hands-on experience with Microsoft Azure ecosystem including Azure OpenAI, AKS, Azure Cosmos DB, Azure App Services, and Azure SQL
- Hands-on experience with Python, .NET, and Angular for designing and implementing platform components
- Proven experience with Kubernetes, CI/CD pipelines, deployment automation, DevSecOps practices, and Infrastructure as Code
- Strong expertise in scalable distributed systems, microservices architecture, asynchronous programming, data structures, and algorithms
- Ability to architect secure, scalable, resilient, highly available web applications and REST API-based systems
- Experience mentoring technical teams and driving engineering governance and platform standards
- Understanding of Industrial IoT protocols and standards such as MQTT and OPC UA
- Strong communication, technical documentation, stakeholder management, and cross-functional collaboration skills
ABB Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about ABB and has not been reviewed or approved by ABB.
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Healthcare Strength — Healthcare coverage is described as comprehensive, with medical, dental, vision, mental health support, and disability and life insurance included. Immediate eligibility in some roles reinforces the sense of dependable core coverage.
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Leave & Time Off Breadth — Time-off offerings are described as broad, including paid holidays, sick days, volunteer time, sabbaticals, and, in some cases, 25 days of PTO. Flexible scheduling and remote-work options add to perceived time-off and flexibility value.
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Retirement Support — Retirement benefits are positioned as robust, including a 401(k) with company contributions or matching and, in some cases, profit sharing or pension savings. Stock purchase/share acquisition programs complement longer-term savings options.
ABB Insights
What We Do
ABB is a leading global technology company that energizes the transformation of society and industry to achieve a more productive, sustainable future. By connecting software to its electrification, robotics, automation and motion portfolio, ABB pushes the boundaries of technology to drive performance to new levels. With a history of excellence stretching back more than 130 years, ABB’s success is driven by about 110,000 talented employees in over 100 countries. www.abb.com
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