AI/ML Engineer

Roles & Responsibilities: -Implement generative AI models, identify insights that can be used to drive business decisions. Work closely with multi-functional teams to understand business problems, develop hypotheses, and test those hypotheses with data, collaborating with cross-functional teams to define AI project requirements and objectives, ensuring alignment with overall business goals.-Conducting research to stay up-to-date with the latest advancements in generative AI, machine learning, and deep learning techniques and identify opportunities to integrate them into our products and services.-Optimizing existing generative AI models for improved performance, scalability, and efficiency.-Ensure data quality and accuracy-Leading the design and development of prompt engineering strategies and techniques to optimize the performance and output of our GenAI models.-Implementing cutting-edge NLP techniques and prompt engineering methodologies to enhance the capabilities and efficiency of our GenAI models.-Determining the most effective prompt generation processes and approaches to drive innovation and excellence in the field of AI technology, collaborating with AI researchers and developers-Experience working with cloud based platforms (example: AWS, Azure or related)-Strong problem-solving and analytical skills-Proficiency in handling various data formats and sources through Omni Channel for Speech and voice applications, part of conversational AI-Prior statistical modelling experience-Demonstrable experience with deep learning algorithms and neural networks-Developing clear and concise documentation, including technical specifications, user guides, and presentations, to communicate complex AI concepts to both technical and non-technical stakeholders.-Contributing to the establishment of best practices and standards for generative AI development within the organization. Professional & Technical Skills: -Must have solid experience developing and implementing generative AI models, with a strong understanding of deep learning techniques such as GPT, VAE, and GANs.-Must be proficient in Python and have experience with machine learning libraries and frameworks such as TensorFlow, PyTorch, or Keras.-Must have strong knowledge of data structures, algorithms, and software engineering principles.-Must be familiar with cloud-based platforms and services, such as AWS, GCP, or Azure.-Need to have experience with natural language processing (NLP) techniques and tools, such as SpaCy, NLTK, or Hugging Face.-Must be familiar with data visualization tools and libraries, such as Matplotlib, Seaborn, or Plotly.-Need to have knowledge of software development methodologies, such as Agile or Scrum.-Possess excellent problem-solving skills, with the ability to think critically and creatively to develop innovative AI solutions.