AI ML Data Science Engineer

2 days 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 SkillsData 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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