Farhan Mohammed

Background

About Farhan Mohammed

Senior AI/ML, NLP Engineer with about 8 years of experience in the dynamic and evolving realms of machine learning, deep learning, big data, natural language processing, and artificial intelligence. Experience in turning data-driven insights into actionable strategies and played a pivotal role in solving complex problems, optimizing processes, and driving innovation across diverse industries. Addressed natural language processing (NLP) challenges by employing transformer-based neural networks, leading to improved sentiment analysis, chatbots, and content recommendation systems. Designed and developed advanced AI models for our LLM application, focusing on natural language understanding (NLU), natural language processing (NLP), and machine learning (ML) algorithm Trained, validated, and optimized machine learning models using frameworks and libraries such as Python, TensorFlow, and PyTorch. Tuned hyperparameters and experimented with different architectures to improve model performance. Continuously expanded skills and knowledge, exploring generative AI, programming languages and industry backgrounds in retail, pharma, financial, health care, and insurance domains. Evaluated and recommended appropriate Azure, AWS and GCP services and OpenAI technologies based on project objectives, scalability, performance, and cost considerations. Preferred experience with LangChain, RAG, LLM Fine Tuning, and LoRA, emphasizing a proactive approach to staying abreast of emerging technologies and frameworks within the machine learning and AI community. Leveraged Large Language Models (LLMs) and Open Source LLMs such as ChatGPT, LLama, Mistral, Bard, and others, utilizing Langchain frameworks to develop innovative solutions and natural language understanding. Applied strong knowledge of data pre-processing, feature engineering, and model evaluation techniques to enhance model accuracy and performance. Demonstrated excellent problem-solving skills while working on challenging AI/ML problems and collaborated with stakeholders, including product managers, data engineers, and others, to understand business requirements and translate them into technical solutions. Successfully deployed machine learning models into production environments, ensuring scalability, reliability, and performance. Integrated models into applications, APIs, and microservices as needed. Expertise in Machine Learning model development and deployment, emphasizing Continuous Integration and Continuous Deployment (CI/CD) processes.