• Join the studio as a Practical Architect • Work with autonomy and efficiency • Own every step of your development

• Operating as the evangelist of the global services and support division, throughout the product life cycle • Creating and sharing a strategic vision for serviceable and supportable products • Defining the technical requirements for product supportability and assess the product against these requirements • Advocating best practice for the customer and internal teams during product design, development and implementation • Owning project launch readiness, ensuring all documentation, policies and processes are in place to deliver successfully to customer

• Design and implement enterprise-scale data platform architecture using Microsoft Fabric. • Lead the design and implementation of CI/CD processes, Git integration, and deployment pipelines for Microsoft Fabric. • Define and promote DevOps, security, governance, and architectural best practices. • Design cloud-native data solutions leveraging Azure Synapse Analytics, Azure Data Factory, Azure Data Lake Storage Gen2, and Azure networking (VNet & Private Endpoints). • Develop and maintain deployment automation using Azure DevOps, YAML Pipelines, ARM Templates, and PowerShell. • Collaborate with Data Engineers, BI Developers, Architects, and business stakeholders to deliver high-quality, scalable solutions. • Drive technical leadership, architectural standards, and continuous improvement across enterprise data platforms.

• Experience building agentic harnesses from scratch, including capabilities such as tool use, multi-step chaining, reasoning, streaming, skills, multimodal integration, RAG, sandboxing, and state management. • Experience designing, building, and operating production APIs and services, including RESTful APIs, streaming APIs, asynchronous workflows, service boundaries, versioning, authentication/authorization, error handling, and backward compatibility. • Strong judgment around when to use synchronous APIs, event-driven architectures, queues, background jobs, or streaming protocols based on latency, reliability, scalability, and user experience requirements. • Demonstrated ownership of production systems, including process improvements, roadmap contributions, defect resolution, uptime and availability monitoring, and operational reliability. • Strong backend engineering fundamentals, with comfort working across service design, APIs, batch workflows, orchestration, observability, and production support. • Familiarity with evaluation techniques such as creating and maintaining evaluation datasets, running offline regression evals, monitoring online production performance, rubric-based scoring, self-verification, human-in-the-loop review, AI-as-judge methods, and quality/reliability analysis at scale. • Clear understanding of foundational machine learning and statistical concepts, including sampling, statistical significance, overfitting/underfitting, precision and recall, and quality tradeoff analysis. • Experience building and orchestrating large-scale batch workflows that use foundation models, including LLMs and VLMs, as well as pretrained open-source models and deep learning models. • Ability to design systems that safely and reliably automate meaningful enterprise workflows using non-deterministic AI components. • Strong judgment around reliability, failure handling, observability, human review, and operational safety. • Deep understanding of how to make systematic tradeoffs between quality, reliability, latency, cost, explainability, and user experience in complex AI systems. • Ability to design pragmatic architectures that balance innovation with production readiness. • Comfort working in environments where model behavior is non-deterministic and system design must account for uncertainty, evaluation, monitoring, and graceful failure. • Experience with both lexical and embedding-based search methods. • Ability to reason about relevance, ranking, latency, recall, precision, indexing strategy, and retrieval performance. • Experience working with foundation models and open-source LLMs beyond simple API calls. • Familiarity with lower-level model behaviors and controls, including temperature, top-p sampling, logprobs, confidence scoring, prompting strategies, and model selection tradeoffs. • Comfort and willingness to build front-end experiences using JavaScript/TypeScript and frameworks such as React. • While the role is primarily backend and AI systems focused, the ability to contribute to user-facing product experiences is important. • Willingness to work across the stack and own the end-to-end product experience is essential. • Familiarity with tools such as FFmpeg, OpenCV, or similar media-processing libraries is a plus.