Generative AI Engineer

Job Summary
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We are seeking a highly skilled Generative AI Engineer<\/b> 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<\/b>.
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Key Responsibilities
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• Design, develop, and deploy Generative AI solutions<\/b> for NLP, Computer Vision, and multi -modal applications.
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• Research, evaluate, and integrate state -of -the -art LLMs<\/b>, including fine -tuning and prompt engineering for enterprise use cases.
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• Build and optimize large -scale data pipelines<\/b> using Azure Databricks<\/b> and Apache Spark<\/b>.
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• Develop and maintain ML Ops pipelines<\/b> for model training, deployment, monitoring, versioning, and lifecycle management.
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• Collaborate closely with data scientists, cloud architects, product owners, and business stakeholders<\/b> to deliver AI -driven solutions.
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• Ensure adherence to Responsible AI<\/b>, data governance, security, and compliance standards.
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• Optimize model performance, scalability, and cost efficiency in cloud environments.
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• Contribute to architectural decisions and best practices for AI and data platforms.
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  Requirements<\/h3>

  Required Skills & Technical Expertise
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  Core Skills
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  • Strong proficiency in Python<\/b> and ML/AI frameworks such as TensorFlow, PyTorch, and Hugging Face<\/b>.
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  • Deep expertise in Generative AI<\/b>, Large Language Models (LLMs)<\/b>, prompt engineering, and model fine -tuning.
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  • Hands -on experience with Azure Databricks<\/b>, Apache Spark<\/b>, and distributed data processing.
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    Cloud & Platform Skills
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    • Solid understanding of Azure cloud architecture<\/b>, including:
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      • Azure Data Lake
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      • Azure Synapse
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      • Azure Machine Learning
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      • Experience designing and deploying cloud -native AI solutions.
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        ML Ops & DevOps
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        • Strong experience with CI/CD pipelines<\/b> for ML workflows.
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        • Hands -on knowledge of ML Ops<\/b>, model monitoring, and version control.
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        • Experience with containerization and orchestration<\/b> tools such as Docker<\/b> and Kubernetes<\/b>.
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          Qualifications
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          • Bachelor’s or Master’s degree<\/b> in Computer Science, Data Science, Artificial Intelligence, or a related field.
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          • Proven experience delivering production -grade AI/ML solutions<\/b> in enterprise environments.
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