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