Today’s Platform Engineer Needs to Build AI-Ready Infrastructure

The demands on today’s platform engineers are evolving at breakneck speed. What began as a natural evolution from DevOps has transformed into a distinct discipline with expanding responsibilities. Today’s platform engineers find themselves at an inflection point: they must not only manage increasingly complex cloud-native environments but also architect the foundation for Artificial Intelligence (AI) across the enterprise. This mandate requires rethinking infrastructure from the ground up to support the unique demands of AI workloads.

It’s undeniable, however, that this role and shift are both necessary. Gartner states that by 2026, 80% of large software engineering organizations will establish platform engineering teams as internal providers of reusable services, components and tools for application delivery — up from 45% in 2022. By 2027, its adoption is expected to significantly impact how infrastructure and operations teams make technology choices, influencing more than half of their decisions.

How Did We Get Here? An Evolution from DevOps to Platform Engineering

DevOps emerged as a cultural and technical movement to break down silos between development and operations teams. It laid crucial groundwork—establishing CI/CD pipelines, infrastructure as code, and a shared responsibility model. But as cloud-native architectures gained more widespread adoption, the complexity of managing distributed systems at scale forced a new specialization.

Platform engineering rose to address this complexity. Rather than expecting every developer to be a Kubernetes expert or every operations engineer to understand the nuances of modern application frameworks, platform teams create abstraction layers that simplify infrastructure consumption. They build internal developer platforms that transform complex infrastructure into self-service capabilities through APIs, interfaces, and automation.

But just as platform engineers have begun to master cloud-native challenges, a new wave of requirements has arrived with the mainstream adoption of AI. AI workloads fundamentally differ from traditional applications in ways that stretch existing platform engineering practices. They demand:

1. Unified access to both real-time and historical data: AI systems, particularly those making autonomous decisions, need seamless access to both streaming data (for real-time context) and historical data (for training and broader patterns);
2. Consistent governance across data domains: When AI systems access data from multiple sources, inconsistent permissions, schemas, or lineage tracking can lead to unreliable results or governance failures; 
3. Efficient data movement: Traditional architectures that separate streaming infrastructure from data warehouses force constant, expensive data movement between systems, introducing latency and exponentially increasing costs; and
4. Horizontal scalability with predictable economics: AI workloads can scale dramatically in unpredictable ways, requiring infrastructure that scales horizontally without linear cost increases.

These points collectively mean the platform engineer now faces a paradox: how to enable rapid AI innovation while maintaining operational stability, governance, and cost efficiency. Talk about a balancing act! 

The Platform Engineer's New Mandate 

Modern platform engineers must abstract infrastructure complexity behind well-designed APIs and self-service interfaces. For AI workloads, this means building platforms that give data scientists and ML engineers programmatic access to training environments with appropriate compute resources, inference environments optimized for latency or throughput, unified data access across streaming and batch domains, and standardized observability and monitoring. 

The most successful platform teams create experiences where AI developers can focus on models and applications rather than infrastructure configuration. A few skillsets that are top of mind for me with this inflection point:

1. Design for Data Proximity. The principle of data proximity (i.e. processing data where it resides rather than moving it) is critical for AI infrastructure. Platform engineers should implement unified storage formats like Apache Iceberg or Delta Lake that work seamlessly across streaming and batch workloads. By leveraging cloud-native object storage as a central foundation for all data types, teams establish a consistent base layer for diverse workflows. This approach should be complemented by zone-aware processing capabilities that minimize expensive cross-region data transfers and leaderless architectures that eliminate costly replication traffic. Collectively, these strategies can reduce infrastructure costs by an order of magnitude while simultaneously improving performance for AI workloads.
2. Unify Governance Across Data Domains. AI systems that access both streaming and historical data introduce unique governance challenges. Platform engineers must implement unified catalog and governance mechanisms that ensure consistent access controls across all data sources while providing centralized schema management and evolution. These systems should maintain end-to-end data lineage tracking and standardized compliance and regulatory controls throughout the data lifecycle. By implementing catalogs that span both real-time and batch data domains, platform engineers can significantly reduce the risk of governance failures in AI systems while simplifying the developer experience.
3. Automate Infrastructure Scaling and Optimization. AI workloads often have unpredictable resource requirements, demanding sophisticated automation from platform teams. Effective platforms incorporate elastic scaling based on actual resource utilization paired with automated optimization of resource allocation. This automation should extend to usage-based pricing and chargeback models that create accountability while maintaining flexibility. Additionally, intelligent placement of workloads based on data locality ensures optimal performance while minimizing unnecessary data movement. These capabilities work together to control costs while ensuring AI applications have the resources they need precisely when they need them without manual intervention.
4. Foster Cross-Functional Collaboration and Skills Development. Perhaps most importantly, platform engineers must bridge traditional organizational silos that can impede AI adoption. This involves collaboration between data engineering, ML engineering, and operations teams through consistent terminology and collaborative practices. Successful platform teams implement common tooling across domains and design platforms that accommodate different expertise levels, from novice data scientists to experienced ML engineers. By building comprehensive educational resources and documentation that empower developers of all backgrounds, platform teams create an environment where AI innovation can flourish throughout the organization rather than remaining isolated in specialized teams.

Unified Infrastructure for the AI Era

The most forward-thinking platform engineers are moving away from separate infrastructure stacks for streaming, batch processing, and AI workloads. Instead, they're building unified foundations that handle these diverse workloads efficiently through common abstractions, storage formats, and governance models.

This unified approach delivers three critical benefits:

1. Dramatically reduced costs: By eliminating redundant infrastructure and expensive data movement, organizations can reduce the total cost of AI infrastructure by up to 80%.
2. Accelerated innovation: When developers can access all data through consistent interfaces without waiting for complex ETL processes, they can iterate on AI applications much faster.
3. Enhanced governance and compliance: A unified approach enables consistent security, privacy, and regulatory controls across all data domains.

As AI becomes central to every aspect of the enterprise, platform engineers hold the key to sustainable adoption and integration. By reimagining infrastructure for this new era—prioritizing data proximity, unified governance, and developer experience—they can enable AI innovation while maintaining the operational excellence that businesses depend on.