AI & Machine Learning • Design, develop, and implement machine learning models for predictive analytics, natural language processing (NLP), computer vision, or recommendation systems. • Lead end-to-end AI project lifecycles, including validation, productionization, and optimization using frameworks such as TensorFlow, PyTorch, and scikit-learn. • Stay updated with the latest advancements in Generative AI and foundation models, evaluating and integrating them as needed. • Partner with cross-functional teams to identify AI use cases and deliver measurable business value. Data Science & Analytics • Lead the end-to-end analytics lifecycle, from data exploration to actionable insights and product development. • Collaborate with data engineers and analysts to build impactful solutions that drive business outcomes. • Leadership & Strategy • Mentor and guide junior team members, fostering their professional growth. • Contribute to the enterprise AI and cloud strategy, including roadmap planning and vision setting. Cloud Architecture & Engineering • Architect and design cloud-native AI and data platforms on GCP (preferred), AWS, or Azure. • Optimize data pipelines and machine learning workflows using services like Vertex AI, SageMaker, or Azure ML. • Ensure scalable, secure, and cost-efficient architectures with robust CI/CD pipelines in cloud environments.
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