MenT

• Define and execute a product roadmap for AI/ML-powered features and products • Work with the technical team to prototype and experiment with the latest AI models (LLMs, multimodal AI, generative systems) using PyTorch, TensorFlow, and Hugging Face • Design AI workflows including prompt engineering, fine-tuning, and RAG systems • Conduct market research and competitive analysis in the AI landscape • Partner with engineering, design, and data science teams to bring AI products to market • Establish success metrics and present product vision to executive leadership • Stay current with emerging AI research and assess applicability to product development

• A Senior ML Engineer is responsible for designing, implementing, and maintaining AI systems across various applications. • They contribute to the organization's AI strategy, work on complex solutions and optimize existing systems to enhance performance. • Responsibilities include mentoring junior ML engineers and collaborating with cross-functional teams. • Develop AI applications and solutions by understanding business needs, collaborating with stakeholders, analyzing data, and implementing AI algorithms. • Design, develop, and maintain robust AI systems, including machine learning models and deep learning networks. • Document and demonstrate solutions with clear technical documentation, diagrams, and code comments. • Contribute to the organization’s AI strategy by researching cutting-edge tools and techniques, participating in educational opportunities, and maintaining professional networks. • Identify and resolve performance and scalability issues in AI applications by improving software and addressing bottlenecks and bugs. • Lead and collaborate with cross-functional teams to define and implement innovative AI solutions, optimizing user interaction and experience. • Conduct code reviews and mentor team members to uphold high coding standards. • Translate business requirements into actionable technical requirements. • Work closely with data engineering and data science teams to implement automated and unit testing. • Improve operations by analyzing systems and recommending procedural changes. • Support engineering goals by delivering project outcomes as needed.