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

• Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, or a related field.
  

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