Senior AI Engineer (Azure Cloud)

Company Description

Blendis a premier AI services provider, committed to co-creating meaningful impact for its clients through the power of data science, AI, technology, and people. With a mission to fuel bold visions, Blend tackles significant challenges by seamlessly aligning human expertise with artificial intelligence. The company is dedicated to unlocking value and fostering innovation for its clients by harnessing world-class people and data-driven strategy. We believe that the power of people and AI can have a meaningful impact on your world, creating more fulfilling work and projects for our people and clients. For more information, visit www.blend360.com

AI Engineer

Key Responsibilities

• Design, develop, and deploy AI/ML models using Azure Machine Learning, Azure Databricks, and Microsoft Fabric.
• Build and operationalize LLM-based solutions leveraging Azure AI Foundry, Azure OpenAI, and Cognitive Services.
• Implement RAG-based architectures using Azure AI Search, Vector Databases, and LangChain or LangGraph frameworks.
• Collaborate with Data Engineers and Architects to integrate AI applications with Microsoft Fabric datasets ensuring governance, data lineage, and reliability.
• Implement MLOps and LLMOps best practices for continuous integration, delivery, and monitoring of AI models using Azure ML Pipelines and Azure DevOps.
•  Optimize model performance and ensure scalability, security, and reliability of deployed solutions.
• Work closely with business and technical stakeholders to translate requirements into AI-driven solutions.
• Stay up to date with emerging trends in AI, GenAI, and cloud-based ML technologies.

Qualifications

• 58 years of experience in building and deploying AI/ML or GenAI solutions in production environments.
• Strong expertise in Azure Machine Learning, Azure Databricks, Microsoft Fabric, Azure AI Foundry, and Azure Cognitive Services.
• Proficiency in Python and major ML frameworks (PyTorch, TensorFlow, Scikit-learn).
• Hands-on experience with RAG architecture, prompt engineering, and LLM-based application development.
• Experience with MLOps/LLMOps pipelines, model tracking, and CI/CD using Azure DevOps.
• Familiarity with data integration across Microsoft Fabric, Azure Data Factory, or Synapse.
• Strong understanding of model lifecycle management, monitoring, and performance optimization using MLflow, App Insights, and Azure Monitor.
• Excellent problem-solving, debugging, and collaboration skills.

  Role & responsibilities

Preferred candidate profile