Generative AI Engineer (ID: 3477)

As a Generative AI Engineer you will:

• Design, build, and deploy Generative AI models for NLP, computer vision, and multi-modal use cases.
• Research, evaluate, and integrate state-of-the-art Large Language Models (LLMs) for enterprise applications.
• Fine-tune foundation models and implement effective prompt engineering strategies.
• Develop and optimize large-scale data pipelines using Azure Databricks and Apache Spark.
• Build, deploy, and monitor ML models using robust MLOps workflows.
• Collaborate closely with data scientists, solution architects, and business stakeholders.
• Ensure AI solutions follow data governance, security, and responsible AI standards.
• Continuously optimize model performance, scalability, and reliability in production environments., * Opportunity to work on enterprise-grade Generative AI and LLM use cases.
• Exposure to large-scale data processing and cloud-native ML architectures.
• A technically strong environment focused on modern MLOps and CI/CD practices.
• Collaboration with experienced AI engineers, data scientists, and architects.

• 4-6 years of hands-on experience in Machine Learning, AI, or Generative AI roles.
• Strong proficiency in Python for ML and data engineering use cases.
• Practical experience with Generative AI, LLMs, and model fine-tuning techniques.
• Hands-on exposure to Azure Databricks, Spark, and distributed data processing.
• Experience working with ML frameworks such as TensorFlow, PyTorch, and Hugging Face.
• Solid understanding of Azure cloud services including Data Lake, Synapse, and Azure ML.
• Working experience with CI/CD pipelines, MLOps practices, and automation.

You Should Possess the Ability to

• Translate business requirements into scalable AI and ML solutions.
• Design end-to-end ML pipelines from data ingestion to production deployment.
• Optimize model performance for accuracy, latency, and cost efficiency.
• Work effectively with cross-functional technical and non-technical teams.
• Implement secure, compliant, and responsible AI solutions.
• Monitor deployed models and handle versioning, retraining, and performance drift.
• Work independently in complex problem-solving environments.
• Adapt quickly to evolving AI technologies and enterprise ML standards.