Senior Machine Learning Engineer

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Code and Theory

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Description

• Collaborate with data scientists and business to augment digital transformation efforts by identifying and piloting use cases. • Discuss the feasibility of use cases along with architectural design with business teams and translate the vision of business leaders into realistic technical implementation. • Play a key role in defining the AI architecture and selecting appropriate technologies from a pool of open-source and commercial offerings. • Select cloud, on-premises or hybrid deployment models, and ensure new tools are well-integrated with existing data management and analytics tools. • Work closely with security and risk leaders to foresee and overturn risks, such as training data poisoning, AI model theft and adversarial samples, ensuring ethical AI implementation and restoring trust in AI systems. • Remain acquainted with upcoming regulations and map them to best practice. • Collaborate with cross-functional teams to define AI use cases, gather requirements, and architect end-to-end AI solutions that align with business goals. • Develop and implement machine learning and deep learning algorithms, models, and frameworks to solve intricate problems and enhance system capabilities. • Oversee data collection, preprocessing, and feature engineering to ensure high-quality input for AI models. • Train and fine-tune machine learning models using large datasets, and rigorously evaluate model performance using appropriate metrics. • Design the AI infrastructure, choose suitable frameworks, libraries, and tools, and ensure scalability, reliability, and efficiency of AI systems. • Build rapid prototypes and proof of concepts (POCs) to demonstrate the feasibility and potential of AI solutions. • Provide technical guidance, mentorship, and support to AI development teams, fostering a culture of innovation and collaboration. • Stay abreast of AI trends, emerging technologies, and best practices, and contribute to the company's AI strategy by proposing innovative solutions. • Lead the deployment of AI solutions into production environments, integrating them with existing systems and workflows. • Continuously optimize AI solutions for speed, efficiency, and accuracy, and address any bottlenecks or issues. • Create comprehensive technical documentation, including design specifications, architecture diagrams, and code comments.

Requirements

• Proven experience as an AI Engineer, Machine Learning Engineer, Data Engineer or AI Architect, with a track record of successful AI solution design and implementation. • Strong programming skills in languages such as Python, Java, or C++, and proficiency in AI libraries and frameworks (TensorFlow, PyTorch, scikit-learn, etc.). • In-depth knowledge of machine learning techniques, algorithms, and model evaluation. • Familiarity with big data technologies, cloud platforms, and distributed computing. • Excellent problem-solving abilities and analytical thinking. • Effective communication skills to collaborate with cross-functional teams and present technical concepts to both technical and non-technical stakeholders. • Demonstrated ability to lead technical projects, drive innovation, and mentor junior team members.