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.
Originally posted on Himalayas