Job Purpose
To lead and deliver AI initiatives with a focus on both traditional machine learning and cutting-edge generative AI. The role demands strong engineering capabilities to build, scale, and productionize models that drive business transformation, operational efficiency, and customer experience.
Key result Areas
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Develop and deploy machine learning models for classification, regression, clustering, and anomaly detection across business domains.
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Design and fine-tune generative AI models (e.g., LLMs, transformers, diffusion models) for applications such as document summarization, synthetic data generation, intelligent assistants, and automated content creation.
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Build robust data pipelines and model serving infrastructure using MLOps best practices to ensure scalability, reliability, and maintainability.
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Collaborate with cross-functional teams to identify AI opportunities and translate business needs into technical solutions.
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Ensure compliance with AI governance, risk strategy, and ethical standards.
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Mentor junior data scientists and contribute to the AI Center of Excellence.
Knowledge, Skills and Experience
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Master’s or PhD in Computer Science, Machine Learning, or related field.
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7+ years of experience in AI/ML, including 2+ years in generative AI.
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Strong engineering skills in Python, SQL, and cloud platforms (Azure, AWS, GCP).
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Experience with MLOps tools (e.g., MLflow, Kubeflow, Airflow) and containerization (Docker, Kubernetes).
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Proficiency in deep learning frameworks (PyTorch, TensorFlow) and LLMs.
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Strong understanding of prompt engineering, fine-tuning, and model evaluation.
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Excellent communication and stakeholder engagement skills.