Jobs at Intermedia Cloud Communications

Design trusted AI-powered experiences across Intermedia products, including copilots, conversational interfaces, recommendations, automation, and agentic workflows. Lead research, UX strategy, interaction design, prototyping, testing, launch, and optimization. Partner with Product, Engineering, AI/ML, Research, Security, Privacy, and Legal teams; evolve design-system patterns; promote transparency, user control, accessibility, and responsible AI; and mentor designers.

Develops revenue through channel partners by prospecting, presenting Intermedia cloud communication solutions, building territory plans, supporting partner business development and marketing, and managing pre- and post-sale relationships. The role coordinates with internal teams to ensure successful partner and customer experiences, while driving sales enablement and executive relationship building. Requires extensive channel or outside sales experience, value-based selling expertise, strong communication skills, and teamwork in a matrixed environment.

Own the roadmap and end-to-end delivery of an AI product area across Intermedia’s communication products. Conduct customer and stakeholder discovery, define requirements, prioritize backlogs, partner with engineering and UX, and manage Agile delivery. Use product metrics, experimentation, customer feedback, and market insights to guide decisions and improve usability. Communicate progress and risks while aligning cross-functional teams on product priorities.

Design, develop, test, and maintain scalable backend services and APIs for Agent Studio. Build orchestration, configuration, integrations, deployment, lifecycle, authentication, authorization, observability, and runtime management capabilities. Collaborate with frontend, AI/ML, product, UX, DevOps, and platform teams to deliver reliable, secure, high-performance services. Troubleshoot complex production issues and contribute to technical designs, code reviews, testing, and continuous engineering improvements.

Designs and leads frontend architecture for real-time, conversational web experiences. Develops reusable components, responsive interfaces, APIs, testing, observability, accessibility, and secure applications. Partners with Product, UX, backend, and ML teams; leads initiatives from design through production; troubleshoots complex issues; conducts reviews; mentors engineers; and establishes standards for performance, reliability, scalability, and maintainability.

Develop and optimize machine learning and generative AI solutions for digital agents, including LLMs, NLP, RAG, retrieval, experimentation, evaluation, and statistical analysis. Build scalable data and ML pipelines using Python, SQL, and Spark; assess model quality, reliability, safety, and business impact; and support production deployment. Collaborate with engineering, product, and data science teams while mentoring less experienced colleagues.

Owns execution of AI and data product initiatives across multiple development teams. Responsibilities include backlog prioritization, Agile ceremonies, requirements and user story creation, release coordination, dependency management, stakeholder communication, roadmap input, and mentoring other product owners. The role translates AI capabilities into buildable requirements and coordinates delivery across engineering, UX, QA, operations, and customer-facing teams.

Designs, develops, and maintains scalable backend services, APIs, and data capabilities for analytics, dashboards, and visualizations. Responsibilities include processing and aggregating structured and unstructured data, optimizing performance, collaborating with frontend, data, UX, product, and DevOps teams, and supporting reliable production deployments. The role also involves technical design, testing, monitoring, troubleshooting, code reviews, and continuous improvement of analytics services.

The Site Reliability Engineer will operate and improve production cloud environments, build infrastructure automation, enhance observability, optimize distributed system performance, and support incident response. The role focuses on reliability for Voice/Unified Communications and AI-enabled services, including real-time media quality, graceful degradation, dependency isolation, and recovery procedures. Responsibilities include capacity planning, release validation, monitoring, troubleshooting, service-level management, and collaboration with development, Voice/UC, and AI engineering teams.

Leads and develops a software engineering team responsible for backend services, APIs, data processing, and production systems. Owns execution across the software development lifecycle, establishes engineering quality and reliability practices, partners with Product, AI/ML, and Data teams, and manages hiring, coaching, performance, and career development. Drives scalable, secure, observable solutions and continuous improvement in a rapidly evolving AI environment.

Lead and develop a software engineering team responsible for scalable backend services, APIs, data processing, and production operations. Own delivery across the software lifecycle, establish quality and reliability practices, partner with Product and AI/ML teams, manage priorities and risks, coach engineers, recruit talent, and build an inclusive culture. The role requires strong backend, cloud, distributed systems, CI/CD, observability, and people leadership experience.

Develop and maintain modern frontend applications, backend services, APIs, data models, dashboards, and analytics experiences. Collaborate with product, UX, backend, data, AI/ML, DevOps, and platform teams to deliver reliable customer-facing products. Responsibilities include integrating AI/ML capabilities, processing data, optimizing performance, maintaining data quality, writing tested code, troubleshooting services, and supporting deployment and observability.

Develop and maintain backend services and REST APIs supporting AI assistants. Integrate large language models, enterprise data, APIs, and workflow automation into customer-facing products. Contribute to AI features such as summaries, knowledge assistance, search, task automation, and agent guidance. Write tested, maintainable code; troubleshoot services and integrations; participate in reviews; and collaborate with product, UX, data, security, QA, and platform teams.

Develop and maintain frontend applications and backend services supporting analytics, dashboards, visualizations, and AI-powered features. Build APIs, data models, processing workflows, and reusable interface components. Integrate AI-generated outputs into customer products, collaborate with product, UX, data, and engineering teams, and support testing, observability, deployment, scalability, and reliability.

Designs and leads sophisticated frontend applications for AI-powered digital agents. Responsibilities include building real-time conversational interfaces, reusable web components, streaming interactions, human handoffs, API integrations, accessibility, security, performance, observability, and analytics. The role influences frontend architecture, leads complex initiatives, conducts technical reviews, troubleshoots production issues, establishes engineering standards, and mentors engineers while collaborating with Product, UX, backend, AI/ML, and platform teams.

Design and maintain scalable backend services, APIs, and data capabilities for analytics, dashboards, visualizations, and AI-powered insights. Process and serve structured and unstructured data, integrate AI/ML outputs, optimize performance and reliability, and collaborate with frontend, data, DevOps, UX, and product teams. Contribute to architecture, testing, monitoring, observability, deployments, troubleshooting, and continuous engineering improvements.

Develop and maintain backend services for AI assistants, integrating large language models, enterprise data, APIs, and workflow automation. Build REST APIs and customer-facing features such as summaries, search, suggested responses, and task automation. Implement validation, monitoring, fallbacks, and human review for AI functionality. Collaborate with product, UX, data, security, QA, and platform teams while troubleshooting services, databases, and integrations.

Develops machine learning and generative AI solutions for digital agents, focusing on LLMs, NLP, RAG, retrieval, experimentation, evaluation, and optimization. Builds scalable data and ML pipelines using Python, SQL, and Spark; analyzes structured and unstructured data; establishes quality and performance metrics; and partners with engineering, product, and data teams to deploy reliable AI solutions in production.

Design and develop scalable backend services and APIs for creating, configuring, testing, deploying, monitoring, and managing AI agents. Build orchestration, workflow execution, integrations, runtime, permissions, observability, and lifecycle capabilities. Collaborate with frontend, AI/ML, product, UX, DevOps, and platform teams to deliver secure, reliable, high-performance AI products and resolve complex production issues.

Design AI-powered user experiences across web, desktop, mobile, and administrative products. Lead UX strategy, research, interaction design, prototyping, usability testing, and post-launch optimization for conversational interfaces, copilots, recommendations, automation, and agentic workflows. Establish trustworthy, transparent, accessible AI interaction patterns and contribute to scalable design systems. Partner with Product, Engineering, AI/ML, Research, Security, Privacy, and Legal teams while mentoring designers and influencing product strategy.