React AI Jobs

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The role involves building end-to-end AI features by architecting and shipping fullstack solutions from React frontends to Python backend services that leverage voice AI and large language models (LLMs) to automate clinical workflows. The engineer will operationalize Voice AI by implementing and fine-tuning audio processing pipelines to ensure Automatic Speech Recognition (ASR) and LLM agents perform accurately in diverse real-world medical environments. They will bridge the gap between model and product by translating complex clinician feedback into technical solutions and rapidly prototyping and deploying improvements to model behavior, prompting strategies, and audio handling. Responsibilities include optimizing for real-time interaction by tuning fullstack performance to handle real-time audio streaming and token generation to minimize latency for a seamless conversational experience. The engineer will partner closely with implementation and clinical teams to shorten feedback loops by shipping critical integrations and feature requests from concept to production rapidly.

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Lead the deployment, performance, and reliability of AI agents operating in live, high-stakes healthcare environments. Architect and scale full-stack systems integrating with EHRs, legacy healthcare platforms, and real-time voice infrastructure. Drive technical strategy for customer-facing solutions, collaborating with product and engineering to translate client needs into scalable architecture. Mentor engineers and set standards for code quality, testing, security, and HIPAA-compliant development. Shape product roadmap by identifying systemic challenges in customer workflows and proposing high-leverage technical solutions.

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Design, build, and maintain full-stack applications powering identity and access management (IAM) experiences. Develop and integrate AI/ML models for identity use cases such as fraud detection, anomaly detection, risk-based authentication, and identity verification. Lead and execute SSO migrations across products and platforms, consolidating authentication flows while minimizing user disruption. Drive domain consolidation initiatives by unifying identity systems, services, and user data models across multiple platforms or brands. Improve developer experience (DevEx) by building internal tools, SDKs, APIs, and documentation that simplify identity integrations. Design and evolve secure, scalable APIs supporting authentication, authorization, and identity data services. Partner closely with Security, Platform, and Product teams to implement and standardize protocols and patterns such as OAuth 2.0, OpenID Connect, SAML, JWT, and zero-trust architectures. Ensure AI-powered identity systems are observable, explainable, and production-ready, with robust monitoring and feedback loops. Balance security, performance, and usability while maintaining high standards for privacy and compliance. Contribute to architectural decisions, technical design discussions, and code quality standards.

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Lead development of Agent Memory, the system that enables AI agents to remember and personalize interactions across conversations, transforming one-off exchanges into continuous relationships that drive measurable business outcomes at scale. Balance competing stakeholder needs including end users expecting personalization, operations teams requiring compliance, and developers needing flexible integration patterns. Define how AI agents should remember, addressing session continuity, long-term relationships, intelligent consolidation, context retrieval, and privacy-preserving personalization. Collaborate closely with engineering teams on distributed systems, ML teams on retrieval and embedding technologies, and infrastructure teams on scalable storage solutions. Serve as a trusted and strategic advisor to customers in partnership with sales, go-to-market, and forward-deployed teams. Lead the product through all stages from concept to execution in collaboration with cross-functional partners.

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Ship production-grade backend and frontend features for core member and provider flows using React, TypeScript, APIs, and data layers, ensuring high polish and reliability. Own features end-to-end, including specification, building, testing, deployment, monitoring, and handling complex state, permissions, and edge cases. Build and maintain robust system hygiene, including instrumentation, dashboards and alerts, CI/CD pipelines, code reviews, and production debugging. Design, implement, and maintain AI-powered workflows comprising tool/function calling, structured outputs, Retrieval-Augmented Generation (RAG), evals, tracing, observability, prompt versioning, and guardrails. Build and operate workflow and agent flows using orchestration patterns similar to Temporal, Dagster, or Airflow, managing retries, idempotency, asynchronous job queues, and failure handling. Collaborate closely with cross-functional partners to deliver reliable, scalable, and user-centric healthcare products.

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The Applied AI engineer at Mistral works within the customer-facing technical team to deploy AI solutions that deliver measurable business impact. Responsibilities include identifying high-value internal use cases across various departments such as engineering, legal, HR, sales, and operations; building end-to-end LLM applications including prompts, RAG pipelines, APIs, simple UIs, deployment, and monitoring; owning the full lifecycle of AI tools from prototype to production, maintenance, and iteration; documenting learnings and sharing insights with product and research teams; and converting successful internal tools into customer demos or case studies where appropriate. The role also involves acting as the first internal customer for these tools to identify edge cases and limitations, improving models through usage feedback.

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Collaborate closely with researchers, AI engineers, and product engineers on complex customer projects, integrating cutting-edge AI models into clients' software products. Design, develop, and maintain scalable and robust full-stack applications, ensuring seamless integration between front-end and back-end systems. Develop complex use cases with customers, providing guidance and ensuring the best production integration with back-end and front-end interfaces. Collaborate with product and science teams to continuously improve product and model capabilities based on customer feedback.

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As a Client Operations Engineer (Growth Expert) at GrowthX, your responsibilities include designing and building custom solutions ranging from AI-powered workflows to full-stack applications that leverage technical SEO, marketing expertise, and technical skills. You will occasionally interface directly with clients and work closely with internal teams to translate client requirements into technical solutions that drive measurable growth. Your work spans the entire development lifecycle from conceptualization and architecture to implementation, testing, and deployment. You will also contribute to internal projects by developing tools for the marketing team and creating micro-applications on top of the AI stack, bridging marketing and engineering in both external client projects and internal innovation. Day-to-day tasks vary and may include updating websites and micro-apps, developing client-specific applications over several months, configuring large language models (LLMs) to power new AI workflows, and building internal tools to streamline operations. You are expected to work in a fully remote environment with flexible scheduling that requires at least 4 hours of overlap with US Pacific Time for synchronous collaboration.

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Own the full product development lifecycle for new platform capabilities and product experiences end-to-end. Collaborate closely with internal customers to understand their problems and implement effective solutions. Work with the research team to share relevant feedback and iterate on applying their latest models.

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Own the technical architecture including backend, frontend, infrastructure, and AI integration. Write code daily to build critical systems. Make stack and design decisions shaping the product for years. Set standards for code quality, testing, and engineering discipline. Integrate large language models (LLMs) and AI workflows into the platform. Work directly with founders on product direction and the technical roadmap. Create clarity and structure in an environment lacking perfect specifications. Mentor and guide other developers technically.