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AI/ML Engineer
Arting Digital
posted 27d ago
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Flexible timing
Key skills for the job
Job Description
Primary Skills - Python, PyTorch, TensorFlow, LLM Models
Roles and Responsibilities:
1. Data Collection, Preprocessing, and Management
- Data Collection: Gather diverse datasets for training LLMs or other machine learning models. This may include text data from various sources like web scraping, databases, or APIs.
- Data Preprocessing: Clean and preprocess raw text data (e.g., tokenization, stemming, lemmatization, removing stopwords) for NLP tasks using tools such as SpaCy , NLTK , or custom preprocessing pipelines.
- Data Augmentation: Create synthetic data or perform augmentation techniques to enrich training datasets, particularly in scenarios where large labeled datasets are scarce.
- Data Pipeline Development: Build automated and scalable data pipelines for continuous data ingestion, cleaning, and feeding into models.
2. Building and Training Machine Learning Models
- Model Selection and Design: Design and implement deep learning architectures for specific use cases (e.g., LLMs for NLP tasks like sentiment analysis, text summarization, question answering).
Model Development Using PyTorch and TensorFlow:
- PyTorch: Build and train custom neural networks using PyTorch , leveraging its dynamic computation graph and flexibility for research and experimentation.
- TensorFlow: Implement scalable, production-ready models using TensorFlow (including TensorFlow Hub and Keras for high-level model building).
- Training Large Models: Train large models like transformers (e.g., BERT , GPT , T5 ) using large-scale datasets. Efficiently handle high computational requirements for these models, potentially using cloud services (AWS, GCP) or GPUs.
- Fine-Tuning Pre-trained Models: Fine-tune pre-trained models like BERT , GPT-3 , or other LLMs on task-specific data to improve performance on downstream applications.
- Model Evaluation: Use evaluation metrics like accuracy, F1 score, BLEU score (for text generation), or perplexity to assess model performance. Perform cross-validation and hyperparameter optimization.
3. Model Optimization and Scaling
- Hyperparameter Tuning: Experiment with hyperparameters (e.g., learning rates, batch sizes, number of layers, dropout rates) to enhance model performance and prevent overfitting.
- Optimization: Use model optimization techniques such as quantization, pruning, and knowledge distillation to reduce the size and improve the inference speed of large models.
- Distributed Training: Implement distributed training using PyTorch Distributed or TensorFlowMirroredStrategy to train large models efficiently across multiple GPUs/TPUs.
4. Model Deployment and Integration
- Model Deployment: Deploy AI/ML models into production environments (e.g., AWS SageMaker , Google AI Platform ) ensuring scalability, security, and robustness.
- API Development: Build APIs or microservices for serving models, enabling real-time predictions or batch processing using frameworks like Flask , FastAPI , or TensorFlow Serving .
- Model Monitoring: Implement monitoring systems to track the performance and accuracy of models in production. Detect model drift or degradation over time and retrain when necessary.
- Scalability and Optimization: Ensure that the models can scale to handle large-scale inference workloads. Use TensorFlow Lite for edge devices or ONNX for cross-framework deployment.
5. Research and Experimentation
- Cutting-Edge Research: Stay up to date with the latest advancements in machine learning, especially in transformer models and NLP, and incorporate state-of-the-art techniques into your work.
- Innovation: Experiment with novel approaches for improving model accuracy, efficiency, or generalization (e.g., new transformer variants, unsupervised pretraining techniques).
- Contributing to Open Source: Contribute to or develop open-source projects that enhance machine learning tools, especially in the field of NLP and LLMs.
Employment Type: Full Time, Permanent
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Flexible timing
Monday to Saturday
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Day Shift
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