JavaScript AI Jobs

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Design and deploy AI solutions by working closely with customers to translate their challenges into functional agents, integrating APIs and data sources to automate real business processes. Prototype quickly, build the first version, get it into production, and refine based on real-world feedback. Collaborate with customer teams across engineering, product, and operations to ensure the agent performs, scales, and delivers measurable outcomes. Own end-to-end delivery from discovery call to deployment, leading the technical build, testing, and iteration to ensure the experience feels natural, human, and on-brand. Drive adoption and expansion by sharing results, training teams, and embedding within the customer organization to uncover new opportunities for automation and scale. Act as the face of Bland by being the customer’s champion, traveling on-site, developing real relationships with customers and stakeholders, hosting training sessions and dinners, and providing unreasonable hospitality.

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Implement and integrate AI functionality into key product features, craft and iterate on prompts to improve LLM reliability and usefulness, build AI-powered flows that feel intuitive and responsive to developers, evaluate and test AI outputs to ensure performance and accuracy, work alongside engineers to deliver robust, production-grade code, stay current with LLM tools, APIs, and best practices, deliver reliable, high-quality AI-powered product experiences, translate product needs into technical AI implementations, tune and test prompts for real-world use cases and developer workflows, collaborate closely with engineers and researchers, and contribute across frontend, backend, and integration layers.

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Lead, hire, and mentor a high-performing team of AI Deployment Engineers supporting Codex customers across strategic accounts. Own the operating model and engagement strategy for Codex deployment efforts, ensuring customers successfully move from pilot to production adoption. Guide teams in designing and implementing AI-enhanced development workflows, automations, and scalable deployment architectures. Act as the senior technical escalation point for complex customer implementations and deployment challenges. Partner with Sales, Product, Research, and Applied Engineering teams to align customer outcomes with product direction and roadmap priorities. Help establish repeatable deployment playbooks, technical patterns, and best practices that enable scaled adoption of AI coding tools. Coach engineers to operate as trusted advisors to engineering leadership and executive stakeholders. Synthesize insights from customer deployments and translate them into actionable feedback for internal teams. Champion safe, reliable, and effective adoption of AI-powered development workflows across industries.

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Forward Deployed Engineers lead complex end-to-end deployments of frontier models in production alongside strategic customers, owning discovery, technical scoping, system design, build, and production rollout while partnering with customer engineering and domain teams. They own technical delivery across multiple deployments from prototype to stable production, build full-stack systems to deliver customer value, embed closely with customer teams to understand needs and guide adoption, scope work, sequence delivery, and remove blockers early. They make trade-offs between scope, speed, and quality, contribute directly in the code when needed, codify working patterns into reusable tools and playbooks, share field feedback to help Research and Product improve models, and keep teams moving through clarity and follow-through.

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The Full Stack Engineer is responsible for configuring and developing "skills" for foundation models like Claude and Gemini to automate content generation, marketing flows, and business processes; developing and scaling applications within the JavaScript/TypeScript ecosystem primarily using React and integrating AI agents and automation; creating automated content generation systems for platforms such as Substack, blogs, social media, and personalized email marketing; designing and implementing sophisticated automations using platforms like Make.com or n8n to bridge data between systems such as updating records in EHR tools like Cerbo; managing PostgreSQL databases (Neon/AWS) and handling deployments within a Linux/Bash-heavy environment; helping to implement and refine voice AI solutions for call overflows and appointment scheduling including GoHighLevel integrations; managing and deploying applications on AWS (EC2) with a focus on security and HIPAA compliance; configuring and implementing AI skills across various foundation models; collaborating with AI automation experts to design scalable, efficient solutions; and establishing coding standards and best practices for AI-driven development.

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Partner with Account Executives to discover and scope customer challenges, designing high-value technical solutions that showcase the ROI of Netomi’s platform. Architect and build agentic workflows that integrate generative AI with APIs, databases, and enterprise tools to power experiences for customers' end users. Develop custom demonstrations, prototypes, and proofs of concept using the Netomi platform tailored to specific clients' use cases. Design, test, and refine prompts and AI orchestration chains to optimize performance, reasoning, and reliability across varied use cases. Communicate complex technical concepts clearly and persuasively to audiences ranging from C-level executives to hands-on engineers. Collaborate with product and engineering teams, contributing insights from customer engagements to inform roadmap priorities. Document and present solution designs, workflows, and technical configurations for both internal and client-facing reference.

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The Salesforce Technical Architect is responsible for designing and delivering scalable, enterprise-grade cloud solutions across the Salesforce ecosystem, including Sales Cloud, Service Cloud, Experience Cloud, Data Cloud, and Agentforce AI agents. The role includes architecting AI-enabled workflows and autonomous agents, designing enterprise data strategies leveraging Salesforce Data Cloud, and defining scalable enterprise integration architectures using MuleSoft or other iPaaS platforms. The architect provides technical leadership for development teams working with Apex, Lightning Web Components, Visualforce, and Salesforce automation frameworks, and may contribute hands-on when necessary. They ensure solutions are high performance, scalable, secure, and capable of handling large data volumes. The role involves partnering with Delivery Managers, consultants, and client stakeholders to translate business requirements into technical architectures, leading design and code reviews, and mentoring developers and consultants. Additionally, the architect evaluates and implements Salesforce AI capabilities, drives innovation initiatives, serves as a technical advisor to clients on architecture and AI adoption, contributes to internal architecture standards and reusable frameworks, and supports presales and solutioning efforts for complex Salesforce engagements.

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The Full Stack Engineer is responsible for building and maintaining backend services in Python, contributing to APIs and microservices with clean and maintainable abstractions, working with datasets and asynchronous processing pipelines, improving performance, reliability, and observability, participating in system design discussions and learning from senior engineers, collaborating closely with frontend and AI/ML engineers, writing production-quality, testable, and secure code, and participating in code reviews to continuously improve.

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The Product Engineer — Search at Firecrawl is responsible for owning the developer-facing search experience by shipping retrieval and ranking improvements from research as a usable product. The role involves shipping the entire product change including API updates, documentation, and examples. The engineer owns the search API end-to-end, ensuring the integration, usage, and build experience is smooth, covering response format, latency, error handling, pagination, and filtering. They use the API themselves to identify friction points before users do, closely monitor user feedback from GitHub, Discord, and support tickets, and translate research insights into product decisions in collaboration with the Search/IR and RL Research Engineers. Additionally, they run fast product experiments by hypothesizing, instrumenting, shipping, measuring, and iterating quickly to improve the search product. They are also expected to raise the bar on developer experience by fixing inconsistencies in response formats, error messages, and documentation proactively.

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Design and build AI agents that outperform human agents in managing complex customer interactions and driving customer retention. Identify cross-customer trends that guide the evolution of Decagon’s agent building platform and research efforts. Experiment with and run evaluations on the latest text and voice models, then integrate them at scale with large enterprise-grade customers.