- 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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