Azure AI Jobs

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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 primary technical subject matter expert on OpenAI Codex for a portfolio of customers, embedding deeply with them to enable their engineering teams and build coding workflows. Partner directly with customers to design and implement AI-enhanced development workflows, from rapid prototyping through scalable production rollout. Build high-quality demos, reference implementations, and workflow automations, using Codex itself as part of the development process. Lead large-format workshops, technical deep dives, and hands-on enablement sessions that help engineering organizations adopt AI coding tools effectively and safely. Contribute technical content including examples, guides, patterns, and best practices to the OpenAI Cookbook to help the broader developer community accelerate their work with Codex. Gather high-fidelity product insights from real customer deployments and translate them into clear product proposals and model feedback for internal teams. Influence customer strategy and decision-making by framing how AI coding tools fit into their software development lifecycle, technical roadmap, and organizational workflows. Serve as a trusted advisor on solution architecture, operational readiness, model configuration, security considerations, and best-practice adoption.

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Develop, train, and optimize machine learning models for various mobile app features. Research and implement state-of-the-art AI techniques to improve user engagement and app performance. Collaborate with cross-functional teams to integrate AI-driven solutions into applications. Design and maintain scalable ML pipelines, ensuring efficient model deployment and monitoring. Analyze large datasets to derive insights and drive data-driven decision-making. Stay updated with the latest AI trends and best practices and incorporate them into development processes. Optimize AI models for mobile environments to ensure high performance and low latency.

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Develop, train, and optimize machine learning models for various mobile app features. Research and implement state-of-the-art AI techniques to improve user engagement and app performance. Collaborate with cross-functional teams to integrate AI-driven solutions into applications. Design and maintain scalable ML pipelines, ensuring efficient model deployment and monitoring. Analyze large datasets to derive insights and drive data-driven decision-making. Stay updated with the latest AI trends and best practices, incorporating them into development processes. Optimize AI models for mobile environments to ensure high performance and low latency.

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The Lead Member of Technical Staff, Inference Infrastructure, is responsible for providing technical leadership across multiple teams, driving the architecture and strategy for deploying optimized NLP models to production in low latency, high throughput, and high availability environments. They lead the design of customized deployments to meet specific customer needs and mentor engineers to raise the technical standards across the team. The role involves contributing to the development, deployment, and operation of the AI platform delivering large language models through easy-to-use API endpoints, and serving as a key point of contact for customers.

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As a Staff Software Engineer on the Core Infrastructure team at Harvey, your responsibilities include designing and building scalable, fault-tolerant infrastructure systems that power Harvey's AI platform across multiple cloud regions. You will own and evolve the multi-cloud infrastructure (Azure, GCP), including Kubernetes orchestration, networking, and container management. You will lead technical initiatives focused on observability, incident response, and operational excellence, building systems for rapid detection and resolution of issues. Architecting and optimizing distributed systems for reliability, including load balancing, quota management, and failover mechanisms, will be part of your role. You will partner with Product Engineering and Security teams to ensure infrastructure accelerates product development, drive infrastructure-as-code practices using tools like Terraform and Pulumi for reproducible deployments, and mentor engineers through code reviews, design reviews, and technical leadership. Representative projects include designing model proxy architecture for handling inference requests, building distributed rate limiting and quota management systems, architecting multi-region deployment strategies for data residency compliance, developing observability infrastructure with SLA monitoring and cost tracking, and leading CI/CD pipeline evolution to improve velocity and stability.

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Develop, train, and optimize machine learning models for various mobile app features. Research and implement state-of-the-art AI techniques to improve user engagement and app performance. Collaborate with cross-functional teams to integrate AI-driven solutions into applications. Design and maintain scalable ML pipelines, ensuring efficient model deployment and monitoring. Analyze large datasets to derive insights and drive data-driven decision-making. Stay updated with the latest AI trends and best practices, incorporating them into development processes. Optimize AI models for mobile environments to ensure high performance and low latency.

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The Applied AI Engineer is responsible for the technical design, implementation, and deployment of AI solutions tailored to the needs of critical infrastructure and sovereign institutions. Responsibilities include individually deploying AI solutions into production for use cases with significant operational and strategic impact, developing state-of-the-art GenAI applications specific to sovereign institutions and critical infrastructure, collaborating closely with researchers, AI engineers, and product teams on complex projects involving advanced fine-tuning, LLM applications, and contributions to open-source codebases. The role also involves participating in pre-sales discussions to understand client needs and provide technical guidance on Mistral's products, and working with product and science teams to improve offerings with a focus on security, compliance, and performance.

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The Senior Full Stack Software Engineer is responsible for owning technical delivery end-to-end, shaping the architecture of ML-powered applications, and leading implementation across cloud services, APIs, and modern front-end frameworks with Claude Code, the Claude Agent SDK, and the Claude API integrated into design, building, and shipping processes. They act as the technical backbone of their project pod, balancing hands-on development with technical leadership, architectural decision-making, and client-facing collaboration. Responsibilities include reducing project risk through proactive ownership of epic-level design and execution, improving technical decision-making via research and evaluation of solutions including AI tooling, increasing client confidence by leading technical discovery sessions and acting as a technical SME, leading feature and epic-level implementation, leading architecture and solution design for moderately complex solutions, championing AI engineering best practices within the pod, providing mentorship and technical leadership including code review and hiring assistance, engaging with clients for technical discovery and scoping, and implementing higher-level testing and quality strategies for deployed solutions and LLM-powered features.

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Collaborate with senior developers to design, build, and test software solutions for the agricultural sector. Contribute to coding, debugging, and documenting software applications. Assist in the development of new features from concept to deployment. Assist in research and development to improve the AI powered Assistant. Help optimize and improve the performance of existing software systems. Participate in code reviews and contribute to development process improvements and best practices. Test software functionality and troubleshoot issues. Collaborate with cross-functional teams such as product managers and data scientists to align technical solutions with business objectives. Stay updated with trends and technologies in software development and agriculture.