LLM Engineer

ABOUT THE ROLE

We'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 & NLP

Machine 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 SKILLS

• 23 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.