Machine Learning Engineer

Details

• Design and development of machine learning solutions, ensuring accuracy, performance, security, and scalability.
• Implement and maintain end-to-end AI/ML pipelines - from data ingestion and feature engineering through to model development, validation, and deployment with guidance from senior engineers on complex architectural decisions
• Instrument AI/ML services with appropriate metrics, logging, and telemetry to monitor model performance and operational health against defined SLOs
• Participate in on-call rotations, executing progressive rollouts and applying standard mitigation strategies to keep production inference services healthy
• Collaborate across planning, design, and code review phases contributing to product and technical discussions, while helping raise overall code quality through thoughtful review feedback
• Bachelor's degree in Computer Science, Mathematics, Statistics, or a related quantitative field, or equivalent practical experience
• 2+ years of experience in machine learning engineering or applied ML, with demonstrated proficiency in Python and at least one ML framework (PyTorch, TensorFlow, or JAX) and familiarity with NLP libraries such as Hugging Face Transformers, NLTK, or SpaCy.
• Experience developing, testing, and deploying small-to-medium scoped ML services or features in a collaborative engineering environment, including model versioning, experiment tracking, and cloud-based infrastructure (AWS, GCP, or Azure)
• Proficiency in Python (preferred) or similar OO language.
• Experience utilizing Large (or Small) Language Models within software systems.
• Excellent written and verbal communication skills with the ability to articulate complex technical concepts to both technical and non-technical audiences.
• Hands-on experience with conversational AI, or LLM fine-tuning and prompt engineering in a production context
• Exposure to agentic AI frameworks such as LangGraph, AutoGen, CrewAI
• Familiarity with MLOps/LLMOps tooling related to maintaining models in production such as testing, versioning, model registry, retraining, and monitoring.