Sr. Machine Learning Engineer - Search

Role overview Own machine learning work from early experiments through model development, deployment, and production monitoring. This role brings together classical machine learning, deep learning, and language model applications to deliver dependable AI systems at scale.

Responsibilities - Develop, evaluate, deploy, and maintain machine learning models and AI-powered features. - Build pipelines covering data ingestion, feature engineering, training, evaluation, and serving. - Create language model solutions using techniques such as prompt design, retrieval-augmented generation, observability, and orchestration frameworks. - Coordinate with backend and data engineering teams to integrate ML into existing infrastructure. - Track production reliability, performance, and fairness, and improve MLOps through experiment tracking, model versioning, CI/CD, and observability.

Requirements - Three or more years in professional ML or AI engineering, with hands-on production delivery experience. - Strong grounding in supervised and unsupervised learning, feature engineering, and model evaluation. - Proficiency in Python and at least one deep learning framework; PyTorch is preferred. - Experience building and consuming APIs with FastAPI, Flask, Django, or similar. - Practical work with language models, including fine-tuning, retrieval-augmented generation, or AI-powered applications. - Experience with cloud platforms and ML tooling, Docker, and sound practices for testing, versioning, and CI/CD. - Advanced written and spoken English for collaboration with English-speaking stakeholders.

Nice to have - Knowledge of vector databases and semantic search, or experience in NLP, information retrieval, or recommendation systems. - Familiarity with model evaluation methods and responsible AI practices.