AI/ML Data Science Engineer

Required Skills:

• Overall 8+ years and must have 5 years of AI Skills
• 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 with retrieval.
• Recommendation Algorithms: Building systems to suggest items to users.
• Neural Collaborative Filtering: Using neural networks for recommendation systems.
• Neural Network: Designing & implementing models.
• Basic Knowledge in Azure Databricks infrastructure.
• Knowledge in Healthcare Domain

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.
• Basic Knowledge in Azure Databricks infrastructure.
• Knowledge in Healthcare Domain.