Azure AI Jobs

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Lead, mentor, and grow a high-performing team of AI/ML engineers, fostering a culture of innovation, technical excellence, and continuous learning. Collaborate cross-functionally with Customer Success, Product Management, Engineering, and Business Development to scope, prioritize, and align AI/ML initiatives with core business objectives. Define and enforce best practices for the full ML lifecycle, including experimentation, code reviews, reproducibility, deployment pipelines, monitoring, and MLOps. Own the technical roadmap for AI/ML capabilities, ensuring alignment with long-term product strategy while rapidly adapting to research findings and market shifts. Drive translation of applied research into production-ready solutions, balancing cutting-edge innovation with pragmatic delivery at startup speed. Establish team processes for prioritization, planning, and technical guidance to optimize execution speed while ensuring reliability, scalability, and quality. Promote a data-driven culture by defining success metrics and KPIs, ensuring technical outputs are measurable, impactful, and tied to business outcomes. Contribute hands-on to technical architecture, model design, and code reviews where appropriate, while balancing technical leadership and management responsibilities. Advocate for responsible and ethical AI practices, ensuring compliance with organizational policies and industry standards.

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As an AI Tutor in Coding at Mindrift, responsibilities include code generation and code review, prompt evaluation and complex data annotation, training and evaluation of large language models, benchmarking and agent-based code execution in sandboxed environments, working across multiple programming languages, adapting guidelines for new domains and use cases, following project-specific rubrics and requirements, and collaborating with project leads, solution engineers, and supply managers on complex or experimental projects. Flexibility and quick adaptation to new requirements are essential.

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As an AI Tutor in Coding on the Mindrift platform, responsibilities include code generation and code review, prompt evaluation and complex data annotation, training and evaluation of large language models, benchmarking and agent-based code execution in sandboxed environments, working across multiple programming languages, adapting guidelines for new domains and use cases, following project-specific rubrics and requirements, and collaborating with project leads, solution engineers, and supply managers on complex or experimental projects. Flexibility and quick adaptation to new requirements are essential.

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As an AI Tutor in Coding, responsibilities include code generation and code review, prompt evaluation and complex data annotation, training and evaluation of large language models, benchmarking and agent-based code execution in sandboxed environments, working across multiple programming languages, adapting guidelines for new domains and use cases, following project-specific rubrics and requirements, and collaborating with project leads, solution engineers, and supply managers on complex or experimental projects. Flexibility and quick adaptation to new requirements are essential.

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As an AI Tutor in Coding on the Mindrift platform, your responsibilities may include code generation and code review, prompt evaluation and complex data annotation, training and evaluation of large language models, benchmarking and agent-based code execution in sandboxed environments, working across multiple programming languages, adapting guidelines for new domains and use cases, following project-specific rubrics and requirements, and collaborating with project leads, solution engineers, and supply managers on complex or experimental projects. Flexibility and quick adaptation to new requirements are essential.

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Act as a technical reference for the team, supporting engineers through design reviews, technical discussions, and hands-on problem-solving. Design, guide, and evolve LLM- and GenAI-based systems such as AI agents, RAG pipelines, and decision-support tools, balancing performance, cost, reliability, and user impact. Influence the architecture and implementation of ML systems across the stack, including data pipelines, experimentation, deployment, and monitoring in production. Define and promote best practices and standards for model evaluation, experimentation, observability, and iteration across ML initiatives. Partner closely with product and engineering teams to shape ML-driven solutions, clarify trade-offs, and ensure alignment with business goals. Lead technically complex or ambiguous initiatives, unblocking teams and driving clarity where requirements or approaches are not well-defined. Improve the maturity of ML infrastructure and workflows to support multiple contributors and use cases over time. Stay current with advancements in GenAI, LLM tooling, and ML systems, and selectively introduce new approaches that provide clear value. Share knowledge through documentation, mentoring, and collaborative problem-solving to raise the technical bar across the organization.

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As a Full-Stack Engineer at Eloquent AI, you will design and build full-stack applications that power AI-driven workflows for enterprise users. You will develop high-performance front-end interfaces for AI agent control, monitoring, and visualization, and build scalable backend services that support real-time AI interactions, knowledge retrieval, and automation. You will optimize AI-powered user interfaces to ensure seamless and intuitive user experiences. Your role involves working closely with AI researchers and ML engineers to integrate large language models (LLMs), Retrieval-Augmented Generation (RAG), and automation into production-ready applications. You are expected to ship robust code with minimal dependencies that performs efficiently in enterprise environments, and continuously iterate and refine AI-driven products, balancing user needs with technical feasibility.

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Design, deploy, and maintain Figure's training clusters. Architect and maintain scalable deep learning frameworks for training on massive robot datasets. Work together with AI researchers to implement training of new model architectures at a large scale. Implement distributed training and parallelization strategies to reduce model development cycles. Implement tooling for data processing, model experimentation, and continuous integration.