Principal AI Platform Engineer

The Opportunity We are seeking a Principal AI Platform Engineer to design and deliver enterprise-grade AI and cloud platform capabilities. You will combine deep hands-on engineering with technical leadership across applied AI, distributed systems, multicloud architecture, industrial data, and developer productivity. You will collaborate with Product, R&D;, Architecture, Cloud Operations, and customer-facing teams to turn complex needs into secure, scalable, reusable, and production-ready solutions.

What You Will Do:

• Design, build, deploy, and operate AI-enabled services, APIs, connectors, libraries, automations, and reusable platform assets.
• Apply Generative AI, LLMs, RAG, agents, orchestration, vector search, evaluation, grounding, safety, monitoring, and cost controls to production use cases.
• Architect cloud-native services using C#, .NET, ASP.NET Core, REST APIs, microservices, containers, Kubernetes, serverless computing, event-driven patterns, and secure service design.
• Lead or contribute to Azure and AWS platform engineering across identity, data, messaging, API management, infrastructure-as-code, CI/CD, observability, and operational readiness.
• Design and execute cloud migrations, including Azure-to-AWS scenarios, with attention to portability, provider dependencies, data, networking, resilience, cost, cutover, and rollback.
• Solve production challenges involving reliability, latency, security, compliance, performance, scalability, and supportability using telemetry, profiling, benchmarks, and load testing.
• Build solutions for industrial and operational data, including time-series data, historians, SCADA/OT, MES, ERP/EAM integration, digital twins, and edge-to-cloud interoperability.
• Use AI-assisted engineering for codebase analysis, modernization, refactoring, migration, performance investigation, and test generation, with measurable validation and human review.

How You Will Lead

• Influence architecture and engineering decisions across complex product initiatives.
• Coach engineers and architects, strengthen engineering practices, and raise quality without relying on direct people-management authority.
• Translate recurring customer needs, implementation friction, and product gaps into reusable assets, reference implementations, and actionable Product and R&D; recommendations.
• Communicate complex technical concepts clearly to engineers, architects, customers, Product, R&D;, and senior stakeholders.

• Typically 10-14 years of relevant experience, or equivalent demonstrated capability, in software engineering, applied AI, cloud or platform engineering, industrial software, or complex product engineering.
• Recognized expertise in applied AI, supported by substantial hands-on experience in cloud-native software, platform engineering, or distributed systems.
• Deep practical experience with modern backend engineering, preferably C#, .NET, ASP.NET Core, APIs, microservices, containers, observability, and secure design.
• Advanced experience with Azure and AWS across compute, containers or Kubernetes, serverless, databases, storage, messaging, identity, secrets, API management, monitoring, infrastructure-as-code, and CI/CD.
• Experience designing production AI systems using LLMs, RAG, agents, vector search, evaluation, grounding, safety, latency, cost, and monitoring practices.
• Experience with cloud migration and multicloud portability, including provider-specific dependencies, identity, networking, data movement, resilience, phased cutover, and rollback.
• Strong understanding of industrial and operational environments, including time-series data, SCADA/OT, historians, MES, ERP/EAM integration, digital twins, and edge/cloud interoperability.
• Strong engineering judgment across architecture, coding, testing, security, observability, deployment, release readiness, maintainability, versioning, documentation, and operational handover. Highly Desirable
• Experience designing, evaluating, and operating agentic systems that automate SDLC activities such as requirements analysis, implementation, code review, test generation and execution, dependency upgrades, and release preparation.
• Experience integrating agents with source control, CI/CD, and engineering tools using scoped permissions, human approval gates, auditable actions, and safeguards for proprietary code and credentials.
• Experience operating AI workloads in regulated, high-availability, industrial, or mission-critical environments.