Genesys

Senior AI Platform Engineer

Genesys

full-time

Posted on:

Location: Massachusetts, New York, North Carolina • 🇺🇸 United States

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Salary

💰 $129,800 - $241,200 per year

Job Level

Senior

Tech Stack

AWSAzureCloudDockerGoGoogle Cloud PlatformGrafanaJavaKubernetesMicroservicesPrometheusPython

About the role

  • Lead the design and delivery of the AI Services platform, providing standardized components for model serving, orchestration, retrieval-augmented generation (RAG) services, and application enablement Define and evolve platform architecture that supports self-service adoption by developers and enterprise-grade production deployments Build scalable APIs, SDKs, and integration patterns to make AI capabilities accessible across the enterprise Implement secure, multi-tenant, and observable infrastructure that meets enterprise requirements for scalability, compliance, and reliability Ensure the platform complies with governance, security, observability, and regulatory requirements Provide self-service tools, documentation, and frameworks that empower teams to adopt AI without redundancy Define SLAs, monitoring, and support frameworks to ensure reliability and accountability Establish best practices and reference architectures for integrating AI into business-critical systems Partner with AI/ML engineers and AI application developers to ensure the platform supports both model-serving and application needs Collaborate with architects and infrastructure teams to align with broader IT strategy and standards Act as a technical leader and mentor, guiding platform engineers and influencing stakeholders on platform-first approaches Advocate for responsible AI practices, ensuring explainability, auditability, and safe AI usage

Requirements

  • Bachelor’s or Master’s degree in Computer Science, Software Engineering, or related field 7+ years of experience in enterprise software or platform engineering, with at least 3+ years working on cloud-native and AI/ML platforms Proven experience designing and building scalable, distributed platforms (microservices, event-driven systems, APIs) Expertise in cloud platforms (AWS, Azure, or GCP) and container orchestration (Kubernetes, Docker) Hands-on experience with AI platforms such as ChatGPT, AWS Q, or similar Strong programming and automation skills (Python, Go, or Java) Experience with MLOps and AI service delivery (model serving, pipelines, monitoring) Familiarity with enterprise security, compliance, and governance frameworks
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