Generative AI Engineer

Job Summary

We are seeking a highly skilled Generative AI Engineer to design build and deploy enterprise-grade AI solutions leveraging Large Language Models (LLMs) multi-modal AI and scalable data platforms. The role focuses on developing advanced Generative AI use cases while ensuring robust ML Ops practices data governance and secure cloud-native deployments on Microsoft Azure.

Key Responsibilities

• Design develop and deploy Generative AI solutions for NLP Computer Vision and multi-modal applications.
  

• Research evaluate and integrate state-of-the-art LLMs including fine-tuning and prompt engineering for enterprise use cases.
  

• Build and optimize large-scale data pipelines using Azure Databricks and Apache Spark.
  

• Develop and maintain ML Ops pipelines for model training deployment monitoring versioning and lifecycle management.
  

• Collaborate closely with data scientists cloud architects product owners and business stakeholders to deliver AI-driven solutions.
  

• Ensure adherence to Responsible AI data governance security and compliance standards.
  

• Optimize model performance scalability and cost efficiency in cloud environments.
  

• Contribute to architectural decisions and best practices for AI and data platforms.
  

Requirements

Required Skills & Technical Expertise

Core Skills

• Strong proficiency in Python and ML/AI frameworks such as TensorFlow PyTorch and Hugging Face.
  

• Deep expertise in Generative AI Large Language Models (LLMs) prompt engineering and model fine-tuning.
  

• Hands-on experience with Azure Databricks Apache Spark and distributed data processing.
  

Cloud & Platform Skills

• Solid understanding of Azure cloud architecture including:
  

  • Azure Data Lake
    

  • Azure Synapse
    

  • Azure Machine Learning
    

• Experience designing and deploying cloud-native AI solutions.
  

ML Ops & DevOps

• Strong experience with CI/CD pipelines for ML workflows.
  

• Hands-on knowledge of ML Ops model monitoring and version control.
  

• Experience with containerization and orchestration tools such as Docker and Kubernetes.
  

Qualifications

• Bachelors or Masters degree in Computer Science Data Science Artificial Intelligence or a related field.
  

• Proven experience delivering production-grade AI/ML solutions in enterprise environments.