Full Stack AI/ML Engineer – Azure
3 weeks ago
Job Title: Full Stack AI/ML Engineer – Azure & Microsoft Fabric
Location: Noida
Experience: 7+ years
Budget: 25-30LPA
Employment Type: Full-Time
Key Responsibilities
• AI/ML & Data Science
• Design, develop, and deploy machine learning models using Python, Azure AI Studio, Prompt Engineering, and ML frameworks.
• Perform data preparation, feature engineering, and model evaluation.
• Integrate ML models into enterprise applications and workflows.
• Data Pipeline & Transformation
• Build and optimize data pipelines in Microsoft Fabric for ingestion, transformation, and semantic modeling.
• Ensure data standardization, quality, and availability for analytics and applications.
• Full Stack & Software Development
• Develop scalable applications using C#, .NET, and modern frontend frameworks.
• Build and integrate REST APIs for data and AI-powered applications.
• Apply software engineering best practices (CI/CD, testing, DevOps).
• Cloud & Platform Engineering
• Deploy solutions on Microsoft Azure (Azure Functions, Data Lake, App Services, AI Studio) and OpenAI LLM Models
• Ensure solutions meet enterprise standards for security, performance, and compliance.
• Collaborate across teams (data scientists, engineers, business stakeholders) to deliver end-to-end solutions.
Technical Skills:
• Proficiency in Python for ML, data science, and pipelines.
• Strong development experience in C#, .NET, and API integration.
• Hands-on experience with Microsoft Fabric for data pipeline & transformation.
• Familiarity with Azure AI Studio and Azure services for deployment.
• SQL knowledge for data modeling and semantic views.
• Exposure to modern frontend frameworks (React, Angular, or Blazor) is a plus.
Experience:
• 3–7 years of professional experience in software engineering, data engineering, or AI/ML development.
• Proven track record of integrating AI/ML capabilities into enterprise applications.
• Experience with cloud platforms (preferably Microsoft Azure).
Soft Skills:
• Strong analytical and problem-solving abilities.
• Ability to collaborate across multidisciplinary teams.
• Excellent communication and documentation skills.
The ideal candidate is equally comfortable working with data pipelines and ML models as with application development and system integration, bridging the gap between data science and software engineering.
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