Senior Machine Learning Engineer

📋 Description

• Take customer problems from idea to working prototype quickly, prove or kill an approach with data
• Train, fine-tune, and evaluate models for matching, classification, and extraction problems. This
• Design and ship RAG systems, embedding-based retrieval, and LLM-powered features on AWS
• Own the full ML lifecycle: experiment tracking, model versioning, deployment, monitoring, and
• Build evaluation that tells us whether something works: labeled golden sets, offline metrics
• Monitor models in production for drift, output quality, latency, and cost per prediction, and fix

🎯 Requirements

• 7+ years in ML engineering, applied data science, or AI engineering
• A track record of shipping ML or AI systems to production and owning them after launch. You can
• Hands-on experience training and evaluating models: feature engineering, experiment design
• Hands-on experience building production applications with LLMs, including RAG, embeddings, and
• Working knowledge of MLOps practices and tooling, such as MLflow, SageMaker Pipelines, Weights
• Experience with AWS for ML workloads (SageMaker, Bedrock, Lambda, ECS/EKS, S3)