AI/ML Engineer

About the Role We are seeking an AI/ML Engineer to design, develop, and deploy machine learning models for real-world applications. This role emphasizes Retrieval-Augmented Generation (RAG), fine-tuning Large Language Models (LLMs), and speech-to-speech model training and evaluation. If you're passionate about advancing AI technologies and enjoy collaborative problem-solving, we'd love to hear from you. Key Responsibilities Model Development: Design and implement machine learning models, focusing on RAG techniques to enhance LLM performance by integrating external data sources. Fine-Tuning: Fine-tune pre-trained LLMs to improve reasoning and logic capabilities, ensuring models are current with the latest information. Speech-to-Speech Modeling: Develop and optimize models for speech-to-speech conversion, emphasizing real-time processing and accuracy. System Integration: Collaborate with cross-functional teams to integrate models into production systems, ensuring seamless deployment and scalability. Continuous Learning: Stay updated on AI/ML advancements, particularly in RAG, LLM fine-tuning, and frameworks like LlamaIndex and LangChain, applying innovative techniques to enhance model performance. Performance Evaluation: Assess and refine models using relevant performance metrics to ensure they meet application requirements. Code Quality: Write efficient, robust, and maintainable production-level code. Required Skills & Qualifications Educational Background: Bachelor's or Master's degree in Computer Science, AI/ML, Data Science, or a related field. Experience: 2-5 years in developing and deploying ML models, with a focus on RAG and LLM fine-tuning. Programming Skills: Proficiency in Python and experience with ML frameworks such as TensorFlow or PyTorch. Specialized Knowledge: Hands-on experience in deep learning, NLP, and speech/audio processing. Framework Proficiency: Experience with LlamaIndex and LangChain for efficient data retrieval, indexing, and building complex AI workflows. Cloud Platforms: Experience with cloud platforms (AWS, GCP) for model deployment and scaling. Optimization Techniques: Knowledge of model optimization methods, including quantization and pruning. MLOps Practices: Familiarity with MLOps, CI/CD pipelines, and containerization tools like Docker and Kubernetes. Problem-Solving: Strong analytical skills and the ability to work in a fast-paced environment. Preferred Qualifications Real-Time Processing: Experience in real-time audio processing and speech AI. Advanced Models: Understanding of transformer-based models and generative AI. Distributed Computing: Knowledge of distributed computing frameworks like Spark. Fill the form - https://forms.gle/gcg6jUoeZMvUxRSX8 Show more Show less