AI Platform Engineer Jobs

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Build and ship production code including integrations, AI agent configurations, and platform logic based on customer workflows and scheduling rules. Own implementations end-to-end by creating and managing technical project plans from scoping through go-live, coordinating internal resources, delegating workstreams, and driving timelines to completion. Architect high-leverage systems and playbooks to transform complex implementations into repeatable, scalable infrastructure. Serve as the primary technical point of contact for customer teams, leading discovery sessions, resolving integration issues, and building trusted advisor relationships. Monitor AI agent performance at scale, diagnose failure modes, and iteratively improve behavior to enhance accuracy, reliability, and patient experience. Drive account growth post-launch by deploying new features, identifying upsell opportunities, and proactively solving emerging pain points. Translate customer feedback and field observations into actionable requirements to shape the product roadmap.

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The AI Backend Engineer will own and evolve the LLM orchestration pipeline, including designing and optimizing the multi-agent orchestration system, implementing parallelization and streaming to reduce response latency, and building prompt management with versioning and A/B testing. They will design retrieval-augmented generation (RAG) systems for accurate contextual responses, work with vector databases, embeddings, and relevance scoring, and optimize for speed and accuracy at scale. The role involves developing production APIs to connect AI capabilities to the frontend, with considerations for authentication, rate limiting, documentation, and designing for future integrations with CRMs and advisor tools. Additionally, the engineer will establish code review practices and testing standards, document architecture decisions, and contribute to technical patents and intellectual property development.

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Design, build, and deploy AI/ML solutions to automate ITSM ticket triage, classification, prioritization, and routing. Develop NLP-based models for ticket summarization, root-cause detection, and resolution recommendation. Implement AI-powered virtual agents / copilots to assist support engineers and end users. Partner with Product Support, SRE, and Engineering teams to understand recurring issues and automate resolution workflows. Build intelligent runbooks and self-healing automation for common incidents and service requests. Enhance knowledge management by auto-generating and updating KB articles from resolved tickets. Integrate AI solutions with ITSM platforms (HALO). Develop APIs, workflows, and event-driven automations across monitoring, logging, and ITSM tools. Ensure seamless handoff between AI systems and human support engineers. Analyze ticket, incident, and operational data to identify automation opportunities. Train, evaluate, and continuously improve ML models using real-world support data. Implement monitoring for model performance, drift, and accuracy in production. Ensure AI solutions meet reliability, security, and compliance standards. Implement guardrails, explainability, and auditability for AI-driven decisions. Contribute to AI governance and responsible AI practices.

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Build and maintain autonomous agent-based solutions that generate real impact on internal teams to better serve customers. Design, architect, and deliver AI solutions by partnering with technical and business teams. Own the end-to-end lifecycle from design through experimentation, deployment, user adoption, impact measurement, and continuous iteration. Mentor and scale expertise by coaching engineers, setting direction for best practices, and acting as a technical sounding board. Collaborate cross-functionally to ensure solutions meet user needs, generate company-wide impact, and align with other Engineering squads.

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Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for a diverse range of use cases. Deploy and operate the core ML inference workloads for the AI Voices serving pipeline. Introduce new techniques, tools, and architecture that improve the performance, latency, throughput, and efficiency of deployed models. Build tools to provide visibility into bottlenecks and sources of instability and design and implement solutions to address the highest priority issues.

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Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for a diverse range of use cases. Deploy and operate the core ML inference workloads for the AI Voices serving pipeline. Introduce new techniques, tools, and architecture to improve performance, latency, throughput, and efficiency of deployed models. Build tools to identify bottlenecks and sources of instability and design and implement solutions to address the highest priority issues.

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Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for various use cases. Deploy and operate core machine learning inference workloads for the AI Voices serving pipeline. Introduce new techniques, tools, and architecture to improve performance, latency, throughput, and efficiency of deployed models. Build tools to identify bottlenecks and sources of instability, then design and implement solutions to address the highest priority issues.

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Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for diverse use cases. Deploy and operate the core ML inference workloads for the AI Voices serving pipeline. Introduce new techniques, tools, and architecture to improve performance, latency, throughput, and efficiency of deployed models. Build tools to gain visibility into bottlenecks and sources of instability and design and implement solutions to address the highest priority issues.

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Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for diverse use cases. Deploy and operate the core machine learning inference workloads for the AI Voices serving pipeline. Introduce new techniques, tools, and architecture to improve the performance, latency, throughput, and efficiency of deployed models. Build tools to identify bottlenecks and sources of instability and design and implement solutions to address the highest priority issues.

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Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for a diverse range of use cases. Deploy and operate the core ML inference workloads for AI Voices serving pipeline. Introduce new techniques, tools, and architecture to improve the performance, latency, throughput, and efficiency of deployed models. Build tools to identify bottlenecks and sources of instability, then design and implement solutions to address the highest priority issues.