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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.

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