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 RESPONSIBILITIES- Design 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 LearningScikit-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 LearningPyTorch 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 ProcessingHugging 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 SKILLS- 2–3 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.

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