AI ML Data Science Engineer

Compunnel, Inc.; Halifax; On-site; CAD 80,000 - 100,000; Full time

Job summary

An innovative firm is seeking a skilled NLP specialist to leverage advanced techniques in natural language processing tailored for healthcare applications. This role involves crafting effective prompts for AI models, fine-tuning pre-trained models, and integrating various AI tools to enhance performance. The ideal candidate will have expertise in speech recognition, sentiment analysis, and machine learning, with a strong foundation in data science. Join a dynamic team where your contributions will drive impactful solutions in the healthcare sector, making a real difference in how medical data is processed and understood.

Qualifications

• Expertise in NLP techniques tailored for healthcare applications.
• Strong skills in model training, evaluation, and fine-tuning.

Responsibilities

• Design and implement NLP models for healthcare data.
• Evaluate AI outputs and refine models for optimal performance.

Skills

Natural Language Processing

Prompt Engineering

Model Fine-Tuning

Speech Recognition

Text-to-Speech

Computer Vision

Data Science

Machine Learning

Risk Prediction

Feature Engineering

Education

Bachelor's Degree in Computer Science or related field

Tools

TensorFlow

Azure Databricks

Job description

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.