Machine Learning Engineer

Role overview Build and operate machine learning systems, with a strong focus on adapting large language models for practical applications. The role spans model training and evaluation, retrieval-augmented generation, deployment, and ongoing optimization, connecting experimental improvements to production use.

Responsibilities - Develop, fine-tune, deploy, and monitor machine learning models. - Build data pipelines for training and evaluation, and run experiments to improve model quality and efficiency. - Create prompt strategies and evaluation methods for language model outputs. - Design retrieval-augmented generation workflows using embeddings, vector stores, and retrieval components. - Work with annotation and quality teams to improve training data. - Integrate models into applications and internal tools; document architectures and technical decisions.

Requirements - Strong Python skills and practical experience with PyTorch or TensorFlow. - Solid understanding of deep learning, transformer architectures, attention, and language model internals. - Experience fine-tuning language models, including methods such as LoRA, PEFT, instruction tuning, or supervised fine-tuning. - Hands-on ability to build retrieval-augmented generation systems and semantic search using embeddings and vector databases. - Experience evaluating model responses, including detecting hallucinations and scoring output quality. - Ability to deploy models through APIs and improve inference latency, throughput, and cost.

Nice to have - Familiarity with vector database tools such as Pinecone, Weaviate, or FAISS. - Experience with prompt design and structured output controls.