The Senior LLMOps Engineer is responsible for operating, scaling, and governing large language model capabilities across the platform. This includes designing orchestration systems, building evaluation pipelines, and implementing observability and cost management strategies for AI workflows.
The Senior Account Executive will present CINC features and benefits to stakeholders while managing a complex sales cycle to drive growth. They are responsible for identifying new leads, attending trade shows, and collaborating with industry partners to promote innovation.
The role involves reproducing and resolving customer software issues to determine root causes and providing professional technical guidance via email or phone. Additionally, the analyst creates user documentation and collaborates with other departments to enhance product functionality.
Lead security reviews across web applications, APIs, and AWS workloads while implementing secure SDLC processes. Conduct red team exercises and assess AI/LLM-based systems for emerging threats and vulnerabilities.
Design and deliver scalable web applications and AI-native platforms while evolving the architecture from a monolith to modular microservices. Collaborate with cross-functional teams to implement robust APIs and mentor other engineers on clean code and system design.
Lead a high-performing data engineering team to build scalable, event-driven data infrastructure and pipelines. Design data models and architectures that support analytics, operational systems, and AI-enabled applications.
Lead the DesignOps and UI Systems organization to build a unified design system and pattern library across multiple product lines. Define the design-to-development workflow and integrate Applied AI and Generative UI patterns to create adaptive user experiences.
Lead and develop a high-performing product engineering team while remaining hands-on in the codebase. Design and build scalable event-driven microservices and influence broader architectural and AI strategies.
Design and lead global quality automation strategies to improve platform stability and speed. Act as a player-coach to embed shift-left quality practices and AI-native testing across engineering teams.
Lead the transformation of the release process from stage-gated to continuous flow using Lean and DevOps principles. Design the system of work for value flow and measure performance using DORA and flow metrics.
Design and build scalable automated quality systems and frameworks across APIs, services, and user interfaces. Partner with engineering teams to embed quality practices early in the software lifecycle through shift-left testing and CI/CD integration.
Lead the design and implementation of AI-augmented enterprise applications and scalable SaaS systems. Drive technical initiatives to integrate AI into operational workflows while mentoring engineers and improving development effectiveness.
Lead Level 2 application support and design automated systems to improve production operations and incident management. Collaborate with engineering and product teams to reduce failure modes and implement AI-enabled support capabilities.
