- Develop train test and deploy machine learning models.
- Build data preprocessing feature engineering and data validation pipelines.
- Work with large datasets and ensure data quality and consistency.
- Implement predictive analytics classification NLP computer vision or recommendation models depending on project needs.
- Optimize model performance accuracy and inference speed.
- Deploy models using cloud platforms or MLOps frameworks.
- Collaborate with data engineers product teams and software engineers.
- Maintain model monitoring versioning and retraining workflows.
Required Skills & Experience:
- Strong knowledge of ML frameworks (TensorFlow PyTorch Scikit-learn).
- Proficiency in Python and ML-ready libraries (NumPy Pandas Matplotlib).
- Experience with model deployment using Docker FastAPI Flask or cloud ML services.
- Understanding of algorithms statistics and data modelling.
- Experience with NLP deep learning or computer vision (as required).
- Familiarity with MLOps tools (MLflow Kubeflow Vertex AI Sagemaker) is a plus.
- Good understanding of data structures and distributed systems.
AI Tools Implementation & Customization
- Installing and running open-source LLMs (Ollama Llama Mistral etc.).
- Integrating LLMs into applications (via API local server containers).
- Designing and building RAG (Retrieval-Augmented Generation) systems.
- Implementing vector databases (Weaviate Pinecone ChromaDB Qdrant).
- Building embeddings pipelines and prompt pipelines.
- Creating automation flows using n8n LangChain FastAPI etc.
- Fine-tuning or customizing models for company-specific tasks.
Develop train test and deploy machine learning models.Build data preprocessing feature engineering and data validation pipelines.Work with large datasets and ensure data quality and consistency.Implement predictive analytics classification NLP computer vision or recommendation models depending on p...
- Develop train test and deploy machine learning models.
- Build data preprocessing feature engineering and data validation pipelines.
- Work with large datasets and ensure data quality and consistency.
- Implement predictive analytics classification NLP computer vision or recommendation models depending on project needs.
- Optimize model performance accuracy and inference speed.
- Deploy models using cloud platforms or MLOps frameworks.
- Collaborate with data engineers product teams and software engineers.
- Maintain model monitoring versioning and retraining workflows.
Required Skills & Experience:
- Strong knowledge of ML frameworks (TensorFlow PyTorch Scikit-learn).
- Proficiency in Python and ML-ready libraries (NumPy Pandas Matplotlib).
- Experience with model deployment using Docker FastAPI Flask or cloud ML services.
- Understanding of algorithms statistics and data modelling.
- Experience with NLP deep learning or computer vision (as required).
- Familiarity with MLOps tools (MLflow Kubeflow Vertex AI Sagemaker) is a plus.
- Good understanding of data structures and distributed systems.
AI Tools Implementation & Customization
- Installing and running open-source LLMs (Ollama Llama Mistral etc.).
- Integrating LLMs into applications (via API local server containers).
- Designing and building RAG (Retrieval-Augmented Generation) systems.
- Implementing vector databases (Weaviate Pinecone ChromaDB Qdrant).
- Building embeddings pipelines and prompt pipelines.
- Creating automation flows using n8n LangChain FastAPI etc.
- Fine-tuning or customizing models for company-specific tasks.
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