Principal AI Platform Engineer – Applied AI & Multicloud

Our Client, an IT Services and Consultant company, is looking for a Principal AI Platform Engineer – Applied AI & Multicloud for their San Leandro, CA/ Pittsburgh, PA/ Scottsdale, AZ location.

Responsibilities

• 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. Page 1 Principal AI Platform Engineer How
• 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.

Requirements

• 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.
• What Success Looks Like You deliver secure, production-ready AI capabilities and reusable engineering assets; improve cloud portability and operational readiness; and demonstrate measurable gains in engineering productivity, software performance, reliability, and customer value.
• This role is designed for a principal-level technical leader who remains meaningfully hands-on.
• It is not primarily a people-management position.
• Top Skills:
• Applied AI | Cloud Platforms | Industrial Software | Multicloud Engineering
• Years of Experience: 14.00 Years of Experience

Why Should You Apply?

• Health Benefits
• Referral Program

Excellent growth and advancement opportunities