AI ML Data Engineer

• Develop and optimize RAG pipelines: document chunking, embedding generation, vector storage, retrieval, reranking, and grounded generation with citations.
• Work on LLM-based applications: fine-tuning open-source models (LLaMA, Mistral, etc.), building prompt strategies, and deploying inference services.
• Contribute to machine learning models (classification, regression, recommendation, anomaly detection) and deep learning architectures (CNNs, RNNs, Transformers).
• Implement robust model evaluation frameworks (accuracy, F1, BLEU, perplexity, hallucination detection, relevance).
• Collaborate with senior engineers on scalable pipelines, guardrails, and integration with enterprise systems.

Required Skills
Programming & Foundations

• Strong in Python, data structures, and algorithms.
• Hands-on with NumPy, Pandas, Scikit-learn for ML prototyping.
• Machine Learning
• Understanding of supervised/unsupervised learning, regularization, feature engineering, model selection, cross-validation, ensemble methods (XGBoost, LightGBM).
• Deep Learning
• Proficiency with PyTorch (preferred) or TensorFlow/Keras.
• Knowledge of CNNs, RNNs, LSTMs, Transformers, Attention mechanisms.
• Familiarity with optimization (Adam, SGD), dropout, batch norm.
• LLMs & RAG
• Hugging Face Transformers (tokenizers, embeddings, model fine-tuning).
• Vector databases (Milvus, FAISS, Pinecone, ElasticSearch).
• Prompt engineering, function/tool calling, JSON schema outputs.
• Data & Tools
• SQL fundamentals; exposure to data wrangling and pipelines.
• Git/GitHub, Jupyter, basic Docker.