JavaScript AI Jobs

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The AI Agent Engineer will lead the building and deployment of enterprise-grade Voice, Chat AI agents and AI Copilot, owning the end-to-end lifecycle of AI Agents including building, integrating, testing, demoing to clients, deploying into production, and tuning performance. Responsibilities include implementation of AI Agents such as prompt design, workflow configuration, integrations, telephony setup, and evaluation frameworks. The role involves client engagement as the primary technical partner, leading demos, communicating progress, gathering feedback, and guiding solutions from concept to production. The engineer will configure systems integrations using APIs, handling authentication, data mapping, error handling, and integrations with CRMs, knowledge bases, and enterprise tools. Telephony integration tasks include setting up SIP/CCaaS/PSTN routing, passing metadata, configuring fallbacks, and troubleshooting call quality. The role requires prompt design and optimization, iterative testing, and performance monitoring to meet targets. The engineer acts as a strategic partner to translate customer requirements into solutions and unblock challenges in security, connectivity, and knowledge ingestion. Collaboration with product and engineering teams to escalate platform gaps and resolve technical issues while driving client implementations independently is also required.

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Assist customer engagements from start to end by running discovery calls and demos, building and maintaining world class agents, participating in customer calls, and serving as the primary point of contact in a fast-paced environment. Own the full agent development life cycle including building and prototyping quickly, setting up CI/CD, monitoring live usage, iterating to targets, debugging live issues, communicating with customers, and documenting best practices to accelerate future projects. Close the feedback loop with product and platform teams by capturing unmet needs, prototyping new features, contributing directly to the codebase, and collaborating with core teams to strengthen the platform for all customers.

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Build and deploy AI Agents including prompt design, workflow configuration, integrations, telephony setup, and evaluation frameworks. Act as the primary technical partner for customers by leading demos, communicating progress, gathering feedback, and guiding solutions from concept to production. Configure and connect systems using APIs, handling authentication, data mapping, error handling, and integrations with CRMs, knowledge bases, and other enterprise tools. Set up telephony systems including SIP/CCaaS/PSTN routing, pass metadata, configure fallbacks, and troubleshoot call quality. Write and refine prompts for LLM-driven agents, monitor performance, and ensure agents meet automation and containment targets. Translate customer requirements into actionable solutions and work consultatively to unblock challenges in security, connectivity, or knowledge ingestion. Collaborate with product and engineering teams to address platform gaps and resolve technical issues, independently driving leading client implementations.

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Design and deliver production-grade AI agents that are highly performant, reliable, and intuitive, driving revenue directly to Sierra's growth across industries like finance, healthcare, and commerce. Take complete ownership and autonomy in the Agent Development Life Cycle from initial pilot through deployment and continuous iteration, including building, tuning, and evolving AI agents in production environments while defining best practices. Work directly with leaders of large enterprises and cutting-edge startups to understand their business challenges and develop AI agents that transform their operations at scale. Guide the evolution of Sierra's core platform through customer interaction, surfacing unmet needs, prototyping new tools and features, and collaborating with research, product, and platform teams to shape AI agent development and Sierra's products.

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As a Senior Full Stack Engineer at Siena, you will own meaningful parts of the platform end to end, taking ambiguous problems and working out the right approach to ship reliable, high-performance systems that real brands depend on every day. You will own features and systems across the full stack including frontend, backend, and infrastructure from problem definition through production. You will partner with product to break down ambiguous scope and ship in iterative, high-impact releases, make real architectural and design decisions in your area and explain the reasoning to the team, and integrate cutting-edge language models into enterprise customer workflows where reliability and safety are critical. You will solve hard engineering problems including API performance, microservices, and scaling across channels and brands. Additionally, you will maintain and improve AWS infrastructure with a DevOps mindset and contribute to raising the bar around you by sharing knowledge, reviewing thoughtfully, and helping teammates level up.

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Serve as the primary technical subject matter expert post-sale for a portfolio of customers, embedding deeply with them to design and deploy GenAI solutions. Engage with senior business and technical stakeholders to identify, prioritize, and validate the highest-value GenAI applications in their roadmap. Accelerate customer time to value by providing architectural guidance, building hands-on prototypes, and advising on best practices for scaling solutions in production. Maintain strong relationships with leadership and technical teams to drive adoption, expansion, and successful outcomes. Contribute to open-source resources and enterprise-facing technical documentation to scale best practices across customers. Share learnings and collaborate with internal teams to inform product development and improve customer outcomes. Codify knowledge and operationalize technical success practices to help the Solutions Architecture team scale impact across industries and customer types.

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Drive rapid engineering efforts to build full-stack applications using Generative AI tools, enabling deep exploration of product ideas, user experiences, and technical feasibility. Apply Generative AI building blocks such as prompt engineering, graph databases, vector databases, agentic frameworks, evaluations, and guardrails to real-world development. Make key decisions around infrastructure and platform choices, balancing short-term prototyping needs with long-term scalability. Collaborate cross-functionally with product, design, and AI experts to create, test, and iterate on new concepts. Use AI-assisted coding tools to enhance productivity and speed of iteration. Gather user feedback and iterate quickly based on insights to improve usability and effectiveness. Deploy and manage applications on cloud infrastructure including AWS, GCP, and Supabase. Build and integrate APIs and third-party services. Participate in architecture discussions and technical planning. Identify and troubleshoot issues across the stack. Contribute to improving development processes, tools, and team practices. Stay current with industry trends and emerging technologies.

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Build end-to-end features across backend and frontend that directly impact customer revenue. Work closely with design, product, and GTM to ship features from idea to production. Translate real-world workflows, including calls, scheduling, and dispatch, into scalable systems. Improve system reliability, performance, and developer velocity as the system scales. Learn fast by working directly with customers and iterating based on real usage.

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Design, build, and launch end-to-end product experiences for image generation and image editing within ChatGPT. Develop highly interactive frontend experiences that make sophisticated AI capabilities feel intuitive, fast, and delightful. Build scalable backend services, APIs, and workflows that power image creation, editing, storage, sharing, and retrieval. Partner closely with researchers to rapidly prototype and productionize new multimodal capabilities. Collaborate with Product, Design, Data Science, and Engineering teams to identify high-impact opportunities and execute against them. Own projects from concept through launch, including technical design, implementation, experimentation, measurement, and iteration. Optimize performance across the stack, from frontend responsiveness and rendering to backend latency, reliability, and scalability. Design systems that can support millions of users generating and interacting with visual content simultaneously. Leverage experimentation and user insights to improve engagement, usability, quality, and product outcomes. Contribute to engineering best practices around architecture, testing, observability, developer productivity, and operational excellence. Help define the future roadmap for AI-powered creative tools and visual experiences.

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Design, build, and operate backend systems that power image generation and image editing experiences in ChatGPT. Develop scalable APIs, services, and infrastructure that support multimodal AI workflows. Optimize reliability, latency, throughput, and cost across large-scale distributed systems. Partner with researchers to productionize new image generation capabilities and bring them to users quickly and safely. Collaborate closely with Android, iOS, web, and full-stack engineers to build seamless end-to-end product experiences. Drive technical architecture decisions across storage, serving, orchestration, and platform systems. Use data and experimentation to identify opportunities for improving user experience, performance, and system efficiency. Help shape engineering culture through technical leadership, mentorship, and operational excellence.