Azure AI Jobs

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The Deployed Engineer will co-architect and co-build production AI agents with customer engineering teams, own the technical win in pre-sales by designing POCs, answering deep technical questions, and guiding evaluations, help customers deploy and operate agent-based applications including conversational agents, research agents, and multi-step workflows, and advise customers post-sale on architecture, best practices, and roadmap-level decisions. They will also run technical demos, trainings, and workshops for developer audiences, surface field feedback, contribute reusable patterns, cookbooks, and example code that scale across customers, and occasionally contribute code upstream when it meaningfully improves customer outcomes.

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As a Solutions Engineer at Encord, you will be the core technical expert for customers building autonomous vehicles, robotics, and physical AI solutions, specializing in LiDAR data, sensor fusion, and perception. Your responsibilities include leading technical discovery with perception teams to understand their sensor stacks, model development pipelines, and data challenges; architecting complete solutions for complex multimodal datasets including LiDAR, camera, and radar fusion, and sensor calibration; acting as the technical authority on handling 3D point clouds, sensor fusion, temporal sequences, and multimodal annotation; building bespoke proofs of concept for LiDAR data ingestion, point cloud processing, coordinate transformations, and sensor calibration; developing custom integrations with robotics/AV stacks such as MCAP, ROS, Apollo, and Autoware; creating technical demos for LiDAR annotation, 3D bounding boxes, semantic segmentation, and multi-sensor fusion; debugging complex issues involving point cloud rendering, sensor calibration matrices, and multimodal data synchronization; guiding prospects through technical evaluations of LiDAR formats, sensor configurations, and annotation requirements; providing expert consultation on 3D annotation best practices, coordinate conventions, and quality control workflows; partnering with Account Executives to co-own technical wins in enterprise sales cycles; translating technical capabilities into business value for CTOs and senior stakeholders; and channeling customer feedback to Product and Engineering teams to shape the product roadmap.

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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.

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The Software Data Engineer will collaborate with Machine Learning, Full-stack engineers, and Science teams to address complex document mining challenges and enhance the capture and modeling of scientific experiments. The role involves scaling data pipelines to transition data quickly and reliably from research to the platform, working with both semi-structured and unstructured data sources. Responsibilities include defining and applying best practices for a broad range of cloud-based technologies, architecting and maintaining robust data pipelines that ingest diverse data sources and use large language models (LLMs) for high-fidelity entity extraction into structured formats. The engineer will implement evaluation frameworks to monitor accuracy, drift, and hallucination rates of extraction models within production pipelines. They will lead or consult on engineering design proposals aligned with the unified Platform Stream roadmap, make independent technical decisions based on the business context, proactively identify and implement project improvements, respond urgently to operational issues, own issue resolution within their responsibility scope, and challenge current practices by proposing new technologies or working methods.

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Co-architect and co-build production AI agents with customer engineering teams. Own the technical win in pre-sales by designing POCs, answering deep technical questions, and guiding evaluations. Help customers deploy and operate agent-based applications such as conversational agents, research agents, and multi-step workflows. Advise customers post-sale on architecture, best practices, and roadmap-level decisions. Run technical demos, trainings, and workshops for developer audiences. Surface field feedback and contribute reusable patterns, cookbooks, and example code that scale across customers. Occasionally contribute code upstream when it meaningfully improves customer outcomes.

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The Lead Machine Learning Engineer will set the technical direction for complex ML projects, balancing trade-offs and guiding team priorities. Responsibilities include designing, implementing, and maintaining reliable, scalable ML/software systems and justifying key architectural decisions. The role involves defining project problems, developing roadmaps, overseeing delivery across multiple work-streams in ill-defined, high-risk environments, and driving the development of shared resources and libraries across the organisation. The engineer will guide other engineers in contributing to these resources, lead hiring processes, make informed selection decisions, mentor multiple individuals to foster team growth, and develop and execute recommendations for adopting new technologies and changing working methods. Additionally, acting as a technical expert and coach for customers, accurately estimating large work-streams, and defending rationale to stakeholders is required.

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Drive strategic technical discovery with Fortune 500 prospects and customers, translating complex business challenges into clear, impactful technical solutions for AI-powered work. Architect and design robust, scalable, and secure generative AI solutions for enterprise clients, leveraging WRITER's platform, APIs, and custom applications to solve critical business problems. Lead the development and execution of compelling proofs of concept (PoCs) and demonstrations, building custom templates and integrating WRITER's capabilities to showcase transformative value and accelerate time-to-value for customers. Serve as a trusted technical advisor to C-suite executives, VPs of Engineering, and AI leaders, guiding their generative AI strategy and collaborating to define enterprise-level architecture roadmaps. Partner closely with WRITER's product and engineering teams, providing critical feedback from customer engagements to influence the product roadmap and ensure solutions meet evolving market needs. Champion the adoption of WRITER's platform and APIs, educating prospects and partners on the potential of generative AI and empowering them to build their own innovative solutions.

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The Machine Learning Engineer is responsible for building and deploying production-grade machine learning software, tools, and infrastructure. They create reusable, scalable solutions that accelerate the delivery of ML systems. They collaborate with engineers, data scientists, and commercial leads to solve critical client challenges. They lead technical scoping and architectural decisions to ensure project feasibility and impact. They define and implement Faculty's standards for deploying machine learning at scale. Additionally, they act as technical advisors to customers and partners, translating complex ML concepts for stakeholders.

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Design, build, and deploy AI/ML solutions to automate ITSM ticket triage, classification, prioritization, and routing. Develop NLP-based models for ticket summarization, root-cause detection, and resolution recommendation. Implement AI-powered virtual agents / copilots to assist support engineers and end users. Partner with Product Support, SRE, and Engineering teams to understand recurring issues and automate resolution workflows. Build intelligent runbooks and self-healing automation for common incidents and service requests. Enhance knowledge management by auto-generating and updating KB articles from resolved tickets. Integrate AI solutions with ITSM platforms (HALO). Develop APIs, workflows, and event-driven automations across monitoring, logging, and ITSM tools. Ensure seamless handoff between AI systems and human support engineers. Analyze ticket, incident, and operational data to identify automation opportunities. Train, evaluate, and continuously improve ML models using real-world support data. Implement monitoring for model performance, drift, and accuracy in production. Ensure AI solutions meet reliability, security, and compliance standards. Implement guardrails, explainability, and auditability for AI-driven decisions. Contribute to AI governance and responsible AI practices.

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As a Senior MLOps Engineer, the responsibilities include leading technical scoping and architectural decisions for high-impact ML systems, designing, building, and deploying production-grade ML software, tools, and scalable infrastructure, and defining and implementing best practices and standards for deploying machine learning at scale across the business. The role also involves collaborating with engineers, data scientists, product managers, and commercial teams to solve critical client challenges and leverage opportunities, acting as a trusted technical advisor to customers and partners by translating complex concepts into actionable strategies, and mentoring and developing junior engineers while actively shaping the team's engineering culture and technical depth.