AI/ML Engineer

Job Requirements

• Understands end to end objectives and develop models that help them to achieve them along with metrics to track their progress

• Analysing the ML algorithms that could be used to solve a given problem and ranking them by their success probability

• Work collaborate closely with cross-functional teams to deliver AI-driven products that reshape industries and user experiences

• Supervising the data acquisition process if more data is needed
• Contribute to creating AI-powered solutions that generate high-quality content, images, text, audio, or other media while pushing the boundaries of creativity and innovation.
• This role requires a deep understanding of machine learning, neural networks, and generative modeling techniques.
• Performance tuning
• Technical Documentation skills

Work Experience

• Stay up-to-date with the latest advancements in generative AI, deep learning, and related fields. Contribute to the research and development of novel generative models, exploring new algorithms and methodologies
• Model Architecture: Design and develop custom generative models tailored to specific applications. Experiment with various neural network architectures
• Data Processing: Prepare and preprocess datasets to be used for training generative models. Apply techniques such as data augmentation, normalization, and transformation to ensure optimal model performance.
• Training and Optimization: Train generative models on large-scale datasets using appropriate frameworks and libraries. Optimize models for efficiency, speed, and quality of generated output.

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  Evaluation Metrics: Develop and implement metrics to evaluate the quality, diversity, and novelty of generated content. Continuously iterate on models to improve these metrics.
• Deployment: Collaborate with Other Digital Teams to integrate generative models into production systems. Ensure scalability, reliability, and efficiency of the deployed models.
• Collaboration: Work closely with cross-functional teams to translate business requirements into generative AI solutions.
• Documentation: Document your research, experiments, and codebase thoroughly. Create clear and concise documentation for fellow engineers and stakeholders.

Continuous Learning: Stay informed about industry trends and emerging technologies. Attend conferences, workshops, and seminars to maintain expertise in the field of generative AI.