Applied AI Engineer Jobs

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Translate business requirements into requirements for AI/ML models; prepare data to train and evaluate AI/ML/DL models; build AI/ML/DL models by applying state-of-the-art algorithms, especially transformers; leverage existing algorithms from academic or industrial research when applicable; test, evaluate, and benchmark the AI/ML/DL models, and publish the models, data sets, and evaluations; deploy models in production by containerizing the models; work with customers and internal employees to refine the quality of the models; establish continuous learning pipelines for models with online learning or transfer learning; build and deploy containerized applications on cloud or on-premise environments.

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As a Senior Staff AI Engineer at Hippocratic AI, you will define the multi-year technical strategy and architectural roadmap for the AI platform encompassing RAG, multi-agent systems, real-time voice, evaluation, and safety, aligning leadership, engineering, and research around it. You will architect foundational platforms and systems used across multiple products, teams, and partners, making critical technical decisions for the company's future. You will partner directly with executive, product, and clinical leadership to translate long-term healthcare goals into technical initiatives. Additionally, you will represent engineering in board-level, partner, and regulatory discussions, identifying and driving both zero-to-one and one-to-n innovations that expand the company's capabilities. You will own the company-wide safety and evaluation standards, setting gates and measurement systems that all agents, partner deployments, and model changes must pass. Furthermore, you will mentor Staff and Senior engineers, influence hiring and leveling, multiplying the impact of engineering teams, and represent Hippocratic AI externally at conferences, through publications, and partner engagements in the healthcare AI community.

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As a Staff AI Engineer at Hippocratic AI, you will set the technical direction for voice-based generative AI in healthcare, architect intelligent systems powering clinically safe healthcare agents, and own one or more core AI domains end-to-end including RAG, agent orchestration, evaluation, or real-time voice. Responsibilities include designing foundational production-grade AI pipelines for voice-based generative healthcare agents incorporating multi-step reasoning, agent orchestration, and evaluation systems; leading cross-functional initiatives with product, clinical, and engineering teams to translate healthcare workflows into safe and scalable AI experiences; representing engineering in clinical and partner conversations; driving innovation using state-of-the-art LLMs, retrieval systems, and streaming architectures; setting standards for AI-native workflows supporting real-time, conversational, and long-running interactions across healthcare contexts; owning safety and evaluation standards across model evaluation, safety testing, and observability; defining production gates for agents; mentoring senior and mid-level engineers; and elevating team quality through code and design reviews.

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Serve as the technical point of contact for healthcare customers, translating ambiguous problems into scalable solutions. Build and deploy AI systems, including RAG pipelines and multi-agent workflows, and develop real-time conversational AI systems. Work with healthcare data integration standards such as HL7 v2, FHIR, REST APIs, and X12, and build pipelines using Python and SQL. Own the full lifecycle of the AI solutions from design to monitoring, including troubleshooting production issues. Collaborate with product, AI research, and clinical teams. Operate onsite at customer locations in Minneapolis, MN, with occasional travel to Hippocratic AI headquarters for strategic planning and team sessions.

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The AI Agent Engineer is responsible for building and deploying AI agents including prompt design, workflow configuration, integrations, telephony setup, and evaluation frameworks. The role involves acting as the primary technical partner for customers by leading regular demos, communicating progress, gathering feedback, and guiding solutions from concept to production. It includes configuring and connecting systems using APIs, handling authentication, data mapping, error handling, and integrating with CRMs, knowledge bases, and other enterprise tools. The engineer sets up telephony systems including SIP/CCaaS/PSTN routing, passes metadata, configures fallbacks, and troubleshoots call quality. Responsibilities also include writing and refining prompts for LLM-driven agents, monitoring their performance, conducting iterative testing, and ensuring agents meet automation and containment targets. The role requires translating customer requirements into actionable solutions and working consultatively to resolve challenges related to security, connectivity, or knowledge ingestion. Additionally, the engineer collaborates with product and engineering teams to escalate platform gaps, resolve technical issues, and independently drive leading client implementations.

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Design and deploy production-grade agents using LangGraph and LangSmith that handle technical support queries, troubleshoot integrations, and guide users through complex onboarding flows. Analyze customer friction points to build self-service AI systems that reduce support volume and improve customer experience. Act as the product owner and technical lead to proactively identify opportunities for improvement, propose architectures, and own the full lifecycle of the systems built. Participate in the feedback loop for the product team by identifying gaps in frameworks and contributing to the LangChain and LangGraph open-source ecosystem. Develop AI-native onboarding workflows that automate documentation retrieval and code generation to help enterprise customers move from prototypes to production faster.

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As a Sr. Applied AI Engineer, you will build reusable AI products by acting as the product owner for your application area, designing, developing, and deploying robust, repeatable Generative AI agents that serve as configurable solutions for customers. You will partner with Solution and Forward Deployed Engineers during sales and implementation projects to understand customer needs, develop standard templates and reusable components to reduce time-to-activation, and solve core challenges. You will synthesize customer feedback to form a clear vision for your agents, iterate on solutions to solve concrete use cases at scale, and treat each agent as a product itself. Additionally, you will collaborate closely with the core product team to prioritize platform features that unblock application development and serve as an expert user consultant during new feature development.

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As a Sr. Applied AI Engineer at Taktile, the responsibilities include building reusable AI products by acting as the product owner for application areas, designing, developing, and deploying robust generative AI agents as configurable solutions for customers. The role requires partnering with Solution and Forward Deployed Engineers during sales and implementation projects to understand customer needs in depth, and developing standard templates and reusable components to reduce activation time and address core challenges. The engineer must synthesize customer feedback into a clear vision for AI agents, iterating solutions to solve concrete use cases at scale, treating every agent as a product itself. Collaboration with the core product team is essential to prioritize platform features that support application development and acting as an expert user consultant during new feature development.

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Lead and grow a small team building applied AI systems for internal operations. Design and build AI-powered automation systems in close proximity to customers. Stay hands-on in architecture and implementation across the full stack. Develop evolving systems spanning developer tools, automation platforms, knowledge graphs, and data systems. Deploy systems directly to internal users and close customers to iterate rapidly based on real-world feedback. Engage frequently with scaled workforces to understand needs and validate solutions. Create systems for visibility and learning in hybrid workforces. Partner with product, research, and ops teams daily.

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Collaborate directly with the Go-To-Market (GTM) team including Account Executives and Solutions Architects to ensure smooth integration and successful deployment of machine learning solutions; build and present compelling demos and proof of concepts (PoCs) that demonstrate the capabilities of AI technology; design, develop, and deploy end-to-end AI-powered applications tailored to customer needs; contribute to the internal machine learning platform by adding features and resolving bugs; integrate and enable new machine learning models into the existing platform or client environments; improve system performance, efficiency, and scalability of deployed models and applications; work closely with partners to enable joint AI solutions and ensure seamless collaboration.