Machine Learning Engineer

Job Summary The Machine Learning Engineer will build and maintain production-grade ML systems supporting forecasting, anomaly detection, intelligent automation, and agentic workflows. The role combines hands-on model development with production deployment, monitoring, and MLOps responsibilities. The engineer will work closely with leadership and cross-functional teams to move AI capabilities from experimentation to scalable production use. Key Responsibilities ML Development & Lifecycle Design, train, and deploy models for forecasting, anomaly detection, classification, ranking, and optimization. Collaborate with analytics teams to engineer and validate high-impact features. Own the full ML lifecycle including problem framing, data exploration, training, evaluation, deployment, and monitoring. MLOps & Production Engineering Deploy and monitor models using modern MLOps pipelines and tooling. Implement CI/CD, automated testing, versioning, and observability for ML systems. Work with Data Engineering to ensure data quality and pipeline reliability. Agentic

AI & LLM

Systems Build modular AI agents that support multi-step workflows. Develop retrieval-based architectures (RAG), semantic search, and evaluation frameworks. Implement safety guardrails and monitoring systems for agentic workflows. Collaboration & Communication Translate business requirements into ML solutions. Document model decisions, results, and limitations. Promote engineering rigor and responsible AI practices. Required Qualifications 2+ years of software engineering or ML engineering experience OR equivalent hands-on ML experience. Strong proficiency in Python and ML frameworks such as PyTorch, scikit-learn, or XGBoost. Experience deploying and monitoring models in cloud environments (GCP preferred). Understanding of supervised/unsupervised learning, forecasting, embeddings, and ranking methods. Experience with LLMs, prompt engineering, and RAG systems. Ability to work with structured and unstructured data at scale. Strong communication and ability to explain models to non-technical audiences. Preferred Qualifications MS in ML, Data Science, Computer Science, Statistics, or related field, OR equivalent ML engineering experience. Experience in startups or high-growth environments. Familiarity with agentic frameworks (LangChain, LangGraph, AutoGen). Background in music, media, entertainment, or royalties-related data. Experience with knowledge graphs or semantic modeling. Contributions to open-source ML projects or research. Familiarity with AI evaluation methodologies.