AI / ML Engineer

- 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