Are you looking for an opportunity to work with the latest Azure offerings and push the limits of cloud computing? Are you inspired to push the boundaries of how intelligent systems transform cloud experiences? Do you want to solve real-life challenges in intelligent cloud and enable customers to achieve more? If so, we have the perfect role for you!

We are part of the Microsoft Specialized Cloud organization, delivering Azure to customers on their premises. Our team is innovating new technologies for managing Edge Devices from Azure in a safe, secure, sovereign, reliable, and scalable model for our global customers across various Azure Geographies. We pride ourselves in developing technologies that enhance customer experience and enable using edge computing to address customer workload needs and managing edge devices in Azure. We build ecosystems that bring intelligence to locations where customers are running their business. We enable real-time decision-making, ultra-low-latency experiences, and trustworthy AI systems that operate securely at the edge and in the cloud. If you’re passionate about AI-driven engineering — from intent understanding and data-driven insights to autonomous orchestration and predictive edge intelligence — this is where your work will have global impact.

As part of this role in the Microsoft Specialized Cloud team, you operate as a senior technical leader shaping the data & analytics foundations that power this org’s products and platforms at a global scale. The engineer will collaborate with Product, Engineering, and Data Science stakeholders from multiple teams to analyse data requirements, understand the data flow across existing and new products, differentiate human vs. AI Agent generated data, lead the design of data models, run the product with an AI first mindset. You will be responsible for identifying data sources, building code to extract raw data, and transforming it into formats compatible with downstream consumers. You will develop tools, do performance monitoring, and perform root cause analysis of issues to ensure robust and reliable data pipelines. You will define architectural direction, influence multi-team roadmaps, and set data engineering standards that enable reliable, secure, and scalable analytics for AI-driven products. To accelerate our Copilot and agent-driven initiatives across Edge you will have to own the technical vision, system design, and implementation strategy for next-generation AI copilots.

The team is supportive of flexible work arrangements and encourages a healthy work-life balance.

If you're ready to take on the challenge of working with highly motivated engineers, with the latest of the Azure offering and taking them to new heights, then we'd love to hear from you! Please apply today and let's build the future together! The team is supportive of flexible work, and you may work from home up to 50% of the time.

Responsibilities

• Design, build, and operate scalable AI systems that power intelligent product experiences, including Copilot and agent-driven workflows.

• Architect and implement backend services that support multi-step AI interactions, including orchestration pipelines, context management, memory/state persistence, and tool execution.

• Integrate large language models (LLMs), APIs, and internal services to enable context-aware, human-in-the-loop experiences across customer scenarios.

• Build and maintain data and inference pipelines that support model training, fine-tuning, evaluation, and real-time inference across diverse data sources.

• Evaluate, benchmark, and tune AI/ML models (LLMs and traditional models) to meet product requirements for accuracy, latency, reliability, and safety.

• Implement robust retrieval, grounding, and knowledge integration mechanisms (e.g., RAG systems, semantic indexing, vector search) to power intelligent applications.

• Collaborate with product managers, software engineers, and researchers to translate product vision into production-ready AI capabilities and measurable outcomes.

• Ensure reliability, observability, and governance of AI systems, including monitoring model performance, data quality, and responsible AI practices.

• Build reusable platforms, APIs, and tools that enable teams to rapidly develop AI-powered features and self-service intelligent applications.