Principal Software Architect

Job description

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 Manager What we believe inABB’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 responsibilities In this role, we are looking for an experienced Principal Architect – Enterprise AI Platforms to join our Industrial Automation Digital Organization. The role requires a highly accomplished technical leader with extensive expertise in enterprise architecture, AI platform engineering, cloud-native technologies, and technical leadership. The candidate will be responsible for defining architecture standards, driving engineering excellence, and leading the design and implementation of next-generation enterprise AI platforms encompassing Copilot ecosystems, Agentic AI frameworks, LLMOps capabilities, and intelligent automation solutions. The work model for the role is: Hybrid This role is contributing to the Digital Industry Analytics / Research & Development function in India. Main stakeholders include Product Management teams, Technical Leads, AI/ML Engineers, DevOps teams, Security and Compliance teams, Cloud Platform teams, Customer Success teams, and Engineering Leadership. You will be mainly accountable for:Product Development & Architecture Leadership • Define enterprise architecture standards, coding guidelines, engineering principles, and platform best practices supporting next-generation AI platforms and intelligent applications. • Architect and design scalable enterprise AI platforms through High-Level Design (HLD) and Low-Level Design (LLD) activities involving Copilot ecosystems, Agentic AI frameworks, Model Context Protocol (MCP) integrations, LLMOps workflows, and AI orchestration systems using Python, .NET, Angular, and cloud-native technologies. • Create architecture-driven Work Breakdown Structure (WBS) plans covering platform capabilities, AI services, orchestration layers, enterprise integrations, and delivery milestones. • Define and implement cybersecurity controls, AI governance frameworks, secure model access mechanisms, prompt safety guardrails, tenant isolation strategies, and regulatory compliance practices across enterprise AI platforms. • Guide engineering teams on software development best practices including coding standards, testing strategies, resiliency patterns, observability frameworks, and advanced debugging methodologies. • Conduct architecture assessments, design reviews, and code reviews to ensure scalability, maintainability, reliability, and compliance with enterprise engineering standards. • Drive implementation of automated testing frameworks, CI/CD pipelines, deployment governance processes, and release strategies for AI-enabled platforms. • Identify, analyze, and resolve performance bottlenecks associated with AI inference pipelines, orchestration frameworks, vector retrieval systems, and distributed application services. • Create and maintain architecture documentation, solution blueprints, AI workflow diagrams, integration architectures, reference implementations, and platform governance documentation. • Collaborate closely with Product Management and Engineering Leadership teams to define product roadmaps, Minimum Viable Product (MVP) strategies, feature prioritization, and long-term platform evolution plans. • Ensure architecture artifacts and engineering documentation remain standardized, accessible, version-controlled, and continuously updated for all stakeholders. • Define enterprise strategies for multi-tenancy, scalability, AI observability, failover mechanisms, caching approaches, asynchronous processing patterns, and resiliency for production-scale AI workloads. Technical Leadership & People Management • Mentor and guide Technical Leads, Senior Engineers, and Platform Engineering teams on architecture principles, engineering excellence, and AI platform best practices. • Provide strategic technical leadership and architectural direction to distributed engineering teams delivering enterprise AI capabilities. • Delegate architecture initiatives, manage technical dependencies, and proactively remove blockers to improve delivery effectiveness and engineering productivity. • Collaborate effectively with Product Management, Cloud Engineering, DevOps, Security, AI/ML, and customer-facing teams to ensure successful platform execution. • Conduct regular architecture governance reviews and communicate technical progress, risks, and recommendations to leadership stakeholders. • Foster a culture of innovation, collaboration, continuous learning, technical excellence, and knowledge sharing across engineering teams. • Drive cross-functional alignment to support enterprise AI platform integrations, scalability objectives, and customer success initiatives. Agile Delivery & Engineering Governance • Work closely with Scrum teams to ensure architectural alignment with Agile delivery methodologies and sprint objectives. • Support sprint planning, backlog refinement, architecture grooming sessions, and proactive identification of technical risks and dependencies. • Champion continuous improvement initiatives focused on enhancing engineering processes, architecture governance practices, and overall platform quality. • Identify opportunities to optimize engineering workflows, deployment automation capabilities, and AI delivery pipelines. • Provide mentorship and coaching to engineering teams on scalable AI engineering practices and effective Agile execution. Qualifications for the role:• Bachelor's or Master's degree in Computer Science, Engineering, Artificial Intelligence, Data Science, or a related technical discipline. • 8+ years of experience in software engineering, enterprise architecture, and technical leadership roles managing complex engineering initiatives. • Expert knowledge of LLMOps principles, Agentic AI systems, Model Context Protocol (MCP) architectures, Natural Language Processing (NLP) frameworks, and distributed AI platforms. • Deep understanding of model lifecycle management, orchestration frameworks, AI governance practices, monitoring strategies, and production-scale AI deployment methodologies. • Proven experience with AI observability frameworks, prompt governance mechanisms, vector databases, Retrieval-Augmented Generation (RAG) pipelines, and AI telemetry systems. • Strong hands-on experience with Microsoft Azure services including Azure OpenAI Service, Azure Kubernetes Service (AKS), Azure Cosmos DB, Azure App Services, Azure SQL Database, and enterprise cloud networking and security architectures. • Strong understanding of scalable distributed systems, advanced data structures and algorithms, asynchronous programming models, and microservices architectures. • Extensive expertise in Kubernetes, CI/CD pipelines, deployment automation frameworks, DevSecOps practices, Infrastructure as Code (IaC), and cloud-native engineering principles. • Demonstrated ability to architect secure, scalable, resilient, and highly available enterprise web applications and RESTful API platforms. • Understanding of Industrial Internet of Things (IIoT) protocols and standards such as MQTT and OPC UA is preferred. • Proven experience mentoring technical teams, driving engineering governance frameworks, and building high-performing engineering organizations. • Excellent communication, documentation, stakeholder management, and cross-functional collaboration skills w