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SeniorFull-timeAzureMachine LearningCI/CDAIML
Salary Range
$26,520 - $35,880 /year
This salary is estimated based on similar roles. The actual salary may vary.
About the Role
We are seeking an experienced AI Manager with deep expertise in Azure AI, Microsoft Fabric, and Machine Learning ecosystems to design and implement enterprise-grade AI solutions.
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—Data 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.
