Senior Applied Machine Learning Engineer, Asset Intelligence
On-site · US
Job Summary
Senior Applied Machine Learning Engineer to guide the technical direction and architecture of Predictive Maintenance and Asset Intelligence initiatives. Lead end-to-end ML systems—from data ingestion and feature engineering to model training, deployment, and monitoring. Mentor ML/data engineers and collaborate with product/engineering to align AI roadmap with customer needs. Design robust data and feedback loops connecting telemetry to model retraining; optimize performance with techniques like quantization and distillation; work with LangChain, LlamaIndex, and Hugging Face to build reasoning systems; ensure production standards for latency, reliability, explainability, and security. 7+ years of experience in ML/AI; expertise in Python; familiarity with PyTorch, TensorFlow, and cloud ML stacks; deployment of production ML systems at scale; strong background in LLMs, time-series modeling, and anomaly detection; leadership in architectural decisions and cross-team collaboration; MLOps tooling knowledge; advanced degree preferred.
Required Qualifications
- 7+ years of experience in Machine Learning, Data Science, or Applied AI
- Proficiency in Python; strong familiarity with PyTorch, TensorFlow
- Experience deploying production ML systems at scale
- Strong background in LLMs, time-series modeling, and anomaly detection
- Ability to lead architectural decisions and mentor engineers
- Knowledge of MLOps tooling (Docker, Kubernetes, Weights & Biases, MLflow, SageMaker)
- Advanced degree (MS/PhD) in Computer Science, Machine Learning, or related field preferred
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