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Senior Software Engineering Manager – Manufacturing Intelligence, Agentic Systems & Physical AI
AppleBengaluru, Karnataka, IndiaSenior12+ years
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Apple’s Manufacturing and Product Operations organization is looking for a hands-on, technically accomplished Senior Software Engineering Manager to lead multiple software engineering teams building the next generation of intelligent manufacturing systems.
This organization develops the software platforms, agentic workflows, data infrastructure, and AI-powered applications that support complex manufacturing operations at global scale. The work spans manufacturing process optimization, production planning, quality inspection, equipment intelligence, supply and material workflows, and the emerging use of embodied and physical AI on the factory floor.
In this role, you will lead multiple teams responsible for building highly scalable, reliable, and secure systems that connect enterprise applications, manufacturing data, AI models, industrial equipment, and human decision-making. You will establish a cohesive technical strategy across these teams and work closely with manufacturing, operations, quality, test, automation, robotics, machine learning, and data engineering organizations to turn emerging technologies into dependable production capabilities.
Building intelligent systems for manufacturing introduces unique challenges: heterogeneous data, rapidly changing factory conditions, high reliability requirements, physical-world constraints, and decisions that can directly affect production. We are looking for an experienced organizational leader with strong software engineering depth, architectural judgment, manufacturing awareness, and a demonstrated ability to build, scale, and align high-performing engineering teams.
As a Senior Software Engineering Manager, you will lead multiple software and systems engineering teams responsible for designing, building, and operating intelligent platforms for manufacturing.You will define the technical strategy, organizational structure, and engineering roadmap across a portfolio of systems that combine distributed software, manufacturing data, machine learning, agentic AI, and physical automation. You will remain deeply engaged in architecture, design reviews, technical trade-offs, and the transition of early prototypes into secure, scalable, and operationally reliable production systems.
- Lead multiple software engineering teams responsible for manufacturing platforms, enterprise integrations, agentic workflows, AI applications and factory-edge systems
- Establish a unified technical vision, platform strategy and multi-year engineering roadmap across the organization
- Define clear charters, ownership boundaries, interfaces and operating mechanisms across teams while ensuring that systems are designed as a cohesive platform rather than a collection of independent solutions
- Develop engineering managers, technical leads and senior individual contributors, creating a strong leadership bench capable of scaling the organization
- Lead the architecture and development of software platforms supporting manufacturing planning, execution, quality, automation and operational decision-making
- Build agentic workflows that can retrieve and reason over manufacturing data, use enterprise and factory tools, execute multi-step processes, validate outcomes and appropriately involve human operators
- Develop the orchestration, tool-calling, memory, context-management, evaluation, observability and governance foundations required to deploy AI agents safely in production
- Establish software architectures that connect AI systems with manufacturing applications such as ERP, MES, PLM, quality systems, supply-chain platforms, equipment telemetry and engineering data sources
- Partner with machine learning teams to productionize models for computer vision, anomaly detection, forecasting, optimization, process intelligence and manufacturing knowledge retrieval
- Enable physical and embodied AI applications involving factory-floor vision, robotics, automated inspection, equipment interaction and closed-loop manufacturing workflows
- Design systems that bridge cloud, on-premises, edge and factory environments while meeting demanding requirements for latency, availability, security, fault tolerance and data governance
- Provide architectural guidance for transitioning proofs of concept and research prototypes into robust, maintainable and high-performance production applications
- Develop reusable platform services, APIs, developer frameworks and integration patterns that enable manufacturing teams to build intelligent workflows efficiently
- Establish rigorous evaluation and validation frameworks for agentic and AI-driven systems, including task success, tool-use accuracy, hallucination containment, operational safety and measurable business impact
- Drive engineering excellence across teams through strong design principles, code quality, automated testing, observability, incident management, operational readiness and service ownership
- Establish consistent engineering processes, development standards, review mechanisms and tooling across the organization
- Manage dependencies and technical alignment across teams and resolve architectural, ownership and prioritization conflicts
- Make sound investment and technical trade-offs across model capability, deterministic software, latency, cost, scalability, reliability and implementation complexity
- Partner with cross-functional leaders to translate manufacturing priorities and operational requirements into a portfolio of clearly defined engineering programs
- Communicate organizational strategy, technical direction, program health, risks and investment needs to senior leadership and cross-functional stakeholders
- Build an inclusive, accountable and innovative engineering culture while maintaining a high bar for execution and technical craftsmanship
- Own organizational planning, hiring strategy, talent development, succession planning, performance management and the growth of engineers and managers across the organization
- 12+ years of software engineering experience, including substantial experience leading multiple engineering teams responsible for large-scale, business-critical systems
- Demonstrated success managing managers, technical leads, senior individual contributors, or multiple engineering workstreams within a complex software organization
- Experience defining organizational strategy, team charters, ownership models, technical roadmaps, and execution mechanisms across multiple teams
- Strong hands-on technical foundation and the ability to provide credible guidance during architecture reviews, design discussions, and complex technical escalations
- Experience building highly available distributed systems, microservices, data platforms, workflow engines, or enterprise integration platforms
- Experience developing systems that interact with relational and non-relational databases, event streams, caching systems, object stores, APIs, and asynchronous processing frameworks
- Strong understanding of system architecture, data structures, algorithms, concurrency, distributed computing, and production reliability
- Experience designing platforms for AI, machine learning, data-intensive applications, or intelligent automation
- Understanding of modern agentic-system concepts, including orchestration, tool use, retrieval, planning, state management, human-in-the-loop controls, evaluations, and observability
- Ability to determine where probabilistic AI approaches are appropriate and where deterministic software, business rules, validation, or operator approval are required
- Experience integrating software with complex enterprise systems, operational workflows, or heterogeneous data environments
- Ability to translate ambiguous manufacturing and business problems into scalable software architectures, organizational plans, and executable engineering programs
- Strong judgment in balancing investments across multiple teams while managing operational risk, technical debt, conflicting priorities, and aggressive schedules
- Excellent written and verbal communication skills, including the ability to explain complex engineering and organizational issues in business and operational terms
- Demonstrated ability to influence and collaborate across large organizations spanning software, machine learning, manufacturing engineering, operations, quality, automation, robotics and program management
- Strong organizational leadership skills and a consistent track record of setting priorities, establishing accountability, resolving cross-team blockers, and delivering measurable outcomes
- Proven ability to hire, mentor, retain, and develop engineers, technical leaders, and engineering managers
- Bachelors or Masters degree in Computer Science, Software Engineering, Electrical Engineering, Robotics, or a related technical field, or equivalent practical experience
- Experience leading software organizations supporting manufacturing, industrial automation, supply chain, quality, test engineering, or factory operations
- Experience with manufacturing systems such as ERP, MES, PLM, QMS, WMS, equipment-control systems, or industrial data platforms
- Experience building AI agents or workflow-automation systems that perform multi-step reasoning and interact with enterprise tools and APIs
- Experience with computer vision systems for automated optical inspection, defect detection, process monitoring, or equipment intelligence
- Exposure to robotics, industrial automation, edge computing, digital twins, simulation, or embodied and physical AI
- Experience deploying AI or software capabilities on factory-floor or edge devices with constrained compute, latency, connectivity, security, or privacy requirements
- Familiarity with technologies such as Java, Python, Spark, Kafka, Kubernetes, Docker, object storage, search platforms, vector databases, and modern cloud or hybrid infrastructure
- Experience developing web applications and operational interfaces using frameworks such as React, Angular, or comparable technologies
- Understanding of AI-system evaluation, model lifecycle management, security, responsible AI, and production governance
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