AI / ML Engineer

Role Overview: As an AI/ML Engineer, your main responsibility will involve developing applications and systems that leverage AI tools and Cloud AI services to create production-ready solutions. You should be proficient in applying GenAI models and technologies like deep learning, neural networks, chatbots, and image processing to address various business challenges effectively. Key Responsibilities: - Implement generative AI models to identify insights for driving business decisions. - Collaborate with cross-functional teams to define AI project requirements and objectives aligned with overall business goals. - Stay updated with the latest advancements in generative AI, machine learning, and deep learning techniques to integrate them into products and services. - Optimize existing generative AI models for improved performance, scalability, and efficiency. - Ensure data quality and accuracy in AI projects. - Lead the design and development of engineering strategies to optimize GenAI model performance. - Utilize cutting-edge NLP techniques to enhance GenAI model capabilities. - Drive innovation in AI technology by determining effective prompt generation processes. - Work with cloud-based platforms like AWS, Azure, or related services. - Demonstrate strong problem-solving and analytical skills. - Develop clear documentation to communicate complex AI concepts to technical and non-technical stakeholders. - Contribute to establishing best practices and standards for generative AI development within the organization. Qualifications Required: - Solid experience in developing and implementing generative AI models, understanding deep learning techniques such as GPT, VAE, and GANs. - Proficiency in Python and experience with machine learning libraries like TensorFlow, PyTorch, or Keras. - Strong knowledge of data structures, algorithms, and software engineering principles. - Familiarity with cloud-based platforms and services such as AWS, GCP, or Azure. - Experience with NLP techniques and tools like SpaCy, NLTK, or Hugging Face. - Knowledge of data visualization tools and libraries like Matplotlib, Seaborn, or Plotly. - Understanding of software development methodologies like Agile or Scrum. - Excellent problem-solving skills with a creative approach to developing innovative AI solutions. Additional Details: - A degree in Computer Science, Artificial Intelligence, Machine Learning, or related fields is a must, and a Ph.D. is highly desirable. - Strong communication skills are essential for effectively conveying technical concepts to diverse audiences. - You should have a proactive mindset and be able to work independently and collaboratively in a fast-paced, dynamic environment.,