We are seeking an experienced AI Engineer with deep expertise in Azure AI, Microsoft Fabric, and Machine Learning ecosystems to design and implement enterprise-grade AI solutions.
The ideal candidate combines strong technical leadership with hands-on experience architecting end-to-end AI/ML systems鈥攆rom data readiness through model deployment鈥攍everaging Azure鈥檚 cloud-native and Fabric-based services.
Key Responsibilities
- Architect and lead the design and implementation of AI/ML solutions on Azure Cloud (Azure Machine Learning, Azure Databricks, Synapse, Azure AI Foundry, Microsoft Fabric, Cognitive Services, Azure OpenAI etc.).
- Define end-to-end AI architecture encompassing data pipelines, feature stores, model training, deployment, and monitoring.
- Partner with stakeholders to translate business challenges into AI-driven solutions and technical blueprints.
- Design scalable and secure architectures adhering to best practices in data governance, MLOps, LLMOps, and cost optimization.
- Integrate Microsoft Fabric as the unified data foundation for AI workloads, ensuring governed, high-quality data access and lineage visibility.
- Lead MLOps initiatives including model CI/CD, versioning, monitoring, and drift detection using Azure DevOps, Azure ML Pipelines, and Azure AI Foundry.
- Contribute to the design, building, or working with event-driven architectures and relevant for asynchronous processing and system integration
- Experience developing and deploying LLM-powered features into production systems, translating experimental outputs into robust services with clear APIs.
- Experience working within a standard software development lifecycle
- Evaluate and implement Generative AI (GenAI) and LLM-based solutions using Azure OpenAI, Cognitive Services, and frameworks such as LangChain or LangGraph.
- Establish observability and monitoring frameworks using Azure Monitor, Application Insights, MLflow, and Databricks dashboards for AI workloads.
- Collaborate with cross-functional teams鈥擠ata Engineers, Data Scientists, Software Engineers and DevOps to ensure seamless integration and delivery of AI products.
- Provide technical mentorship and architectural guidance to engineering teams.