Application Developer-Azure Cloud Modernization

Introduction

A career in IBM Consulting is rooted by long-term relationships and close collaboration with clients across the globe. Youll work with visionaries across multiple industries to improve the hybrid cloud and AI journey for the most innovative and valuable companies in the world. Your ability to accelerate impact and make meaningful change for your clients is enabled by our strategic partner ecosystem and our robust technology platforms across the IBM portfolio

Your Role And Responsibilities

We are seeking a talented GenAI Engineer to design and implement cutting-edge generative AI solutions leveraging Azure AI services and Foundry platform capabilities. This role involves building robust AI/ML pipelines, fine-tuning large language models, and integrating GenAI capabilities into enterprise workflows.

Responsibilities

• Design, develop, and deploy GenAI solutions using Azure AI services and Foundry platform.
• Build and maintain Python-based AI/ML pipelines, scripts, and applications for model training, evaluation, and deployment.
• Collaborate with data scientists, architects, and business stakeholders to translate requirements into technical solutions.
• Fine-tune and optimize large language models (LLMs) and other generative models for specific use cases.
• Implement prompt engineering strategies to improve model performance and relevance.
• Integrate GenAI models into enterprise applications and workflows using APIs, SDKs, and Azure ML pipelines.
• Monitor and troubleshoot deployed models, ensuring reliability and scalability.
  
  

• Strong experience in Python development for AI/ML applications.
• Hands-on experience with Azure AI services, Azure ML, and related cloud technologies.
• Knowledge of LLM fine-tuning, prompt engineering, and generative AI frameworks.
• Familiarity with ML pipelines, model deployment, and monitoring.
• Understanding of API integration and enterprise application workflows.
  
  

• Experience with Foundry platform and enterprise AI integration.
• Exposure to MLOps practices, CI/CD for ML models, and containerization (Docker/Kubernetes).
• Knowledge of data engineering concepts and distributed systems.
• Familiarity with security best practices for AI solutions.
• Strong collaboration and communication skills for cross-functional projects.