AI ML Data Science Engineer

4 weeks ago

Halifax, Canada Compunnel, Inc. Full time

NLP for Healthcare : Specialized natural language processing techniques tailored for medical data. Prompt Engineering : Crafting effective prompts for AI models, especially important for large language models (LLMs). Multimodal Prompting : Designing prompts that work across different AI tools and models. Evaluation and Refinement : Assessing AI outputs and refining prompts for better results. Model Fine-Tuning : Adjusting pre-trained models to improve performance on specific tasks. Speech Recognition : Converting spoken language into text. Text-to-Speech : Generating spoken language from text. Audio Signal Processing : Analyzing and manipulating audio signals. Speech to Text Expertise : Advanced skills in converting speech to text accurately. Sentiment & Tone Analysis Expertise : Analyzing emotions and tone in text data. LLM Expertise : Working with large language models like GPT-4. Computer Vision (Image Processing & OCR) : Analyzing and interpreting visual data, including optical character recognition. Embeddings Models (TensorFlow/Phoenix) : Using embeddings for various ML tasks. Expertise in Knowledge Retrieval Systems & LLM Integration : Integrating retrieval systems with large language models. Recommendation Algorithms : Building systems to suggest items to users. Neural Network : Designing & implementing models. Basic Knowledge in Azure Databricks Infrastructure . Data Science and Machine Learning Skills Data Annotation & Labeling : Essential for creating high-quality training datasets. Model Training : Building and training machine learning models. Fine Tuning : Adjusting pre-trained models to improve performance on specific tasks. Supervised & Unsupervised Learning : Techniques for both labeled and unlabeled data. Risk Prediction (Time Series Models - LSTMs, ARIMA) & Survival Analysis Techniques: Predicting future events and analyzing time-to-event data. Model Evaluation, Selection & Fine-tuning : Assessing and optimizing model performance. Dimensionality Reduction : Reducing the number of features in a dataset. Vector Search Optimization : Enhancing search algorithms using vector representations. Feature Engineering : Creating new features from raw data to improve model performance. Data Drift Monitoring & Identification : Detecting changes in data distributions over time. Synthetic Data Generation : Creating artificial data for training models when real data is scarce. #J-18808-Ljbffr

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