LLM Engineer

ABOUT THE ROLEWe're looking for a skilled LLM Engineer with a solid data science foundation to design, build, and maintain systems leveraging large language models turning cutting-edge capabilities into reliable, scalable product features.KEY RESPONSIBILITIESDesign and implement LLM pipelines: prompt engineering, RAG, and fine-tuning workflows.Build, train, and evaluate ML/DL models for classification, regression, and clustering tasks.Develop NLP pipelines: NER, text classification, summarization, and sentiment analysis.Perform EDA, feature engineering, and statistical modelling on structured/unstructured data.Integrate LLM APIs (OpenAI, Anthropic, Mistral, open source) into production services.Collaborate with backend engineers to serve models at scale with appropriate guardrails.Build tooling for model evaluation, A/B testing, and iterative prompt improvement.FOUNDATIONAL SKILLS ML, DL & NLPMachine Learning Scikit-learn XGBoost / LightGBM Pandas / NumPy Hyperparameter tuning Core algorithms: regression, decision trees, random forests, SVMs, and ensembles.Full ML lifecycle: data cleaning, feature engineering, training, evaluation, and deployment.Evaluation metrics: F1, AUC-ROC, RMSE based on task type. Cross-validation best practices.Deep Learning PyTorch TensorFlow / Keras Transformers LoRA / PEFT GPU training Build and train neural networks CNNs, RNNs, LSTMs, and Transformer architectures.Transfer learning and fine-tuning with LoRA/PEFT. Mixed-precision GPU training.Attention mechanisms, positional encoding, and multi-head attention fundamentals.Natural Language Processing Hugging Face spaCy / NLTK BERT / GPT Semantic search Embeddings NLP fundamentals: tokenisation, stemming, POS tagging, NER, dependency parsing.Word2Vec, GloVe, FastText, and contextual embeddings (BERT, sentence-transformers).Text classification, summarisation, Q&A, and sentiment pipelines in production.Semantic search, dense retrieval, and embedding-based similarity for RAG systems.LLM-SPECIFIC SKILLS23 yrs experience, with 1+ year hands-on with LLMs.Prompt engineering, few-shot learning, chain-of-thought, and instruction tuning.RAG pipelines with vector DBs (Pinecone, Weaviate, Chroma, pgvector).LLM orchestration: LangChain or LlamaIndex.Open-source models via Ollama / vLLM for local inference.REST APIs and scalable Python backend services.Cloud platforms: AWS, GCP, or Azure.