Who you are

• 7+ years of experience in Machine Learning, Data Science, or Applied AI
• Expertise in Python, and strong familiarity with PyTorch, TensorFlow, and cloud ML stacks (AWS, Databricks, or similar)
• Proven experience deploying production ML systems—not just prototypes—at scale
• Strong background in LLMs, time-series modeling, and anomaly detection for real-world data
• Demonstrated ability to lead architectural decisions, mentor engineers, and collaborate across product, data, and platform teams
• Knowledge of MLOps tooling (Docker, Kubernetes, Weights & Biases, MLflow, SageMaker)
• Advanced degree (MS/PhD) in Computer Science, Machine Learning, or related field preferred
• Experience with OCR for extracting structured data from documents
• Background in time-series modeling for predictive maintenance and anomaly detection
• Familiarity with Industrial IoT systems (sensors, telemetry, edge computing)
• Experience applying reinforcement learning or agentic architectures for decision-making and control systems
• Contributions to open-source ML frameworks or research in reliability, explainability, or digital twins

What the job involves

• We are seeking a highly skilled and motivated Senior Applied Machine Learning Engineer to guide the technical direction and architecture of our Predictive Maintenance and Asset Intelligence initiatives
• You’ll combine deep ML expertise with strong software engineering and leadership skills—mentoring engineers, scaling systems, and driving the roadmap for AI-enabled maintenance intelligence across thousands of industrial sites
• This role sits at the intersection of ML architecture, IoT data systems, and product impact, shaping the foundation for MaintainX’s predictive and generative AI strategy
• Lead technical direction for predictive maintenance, anomaly detection, and LLM-powered intelligence across MaintainX products
• Architect end-to-end ML systems—from data ingestion and feature engineering to model training, deployment, and monitoring
• Mentor a growing team of ML and data engineers, instilling best practices for experimentation, evaluation, and model lifecycle management
• Partner with product and engineering leaders to align AI roadmap with customer needs and business goals
• Design reliable data and feedback loops that connect customer telemetry and operator feedback to model retraining
• Drive performance optimization through techniques like quantization, distillation, and scalable inference serving
• Work with LLM frameworks (LangChain, LlamaIndex, Hugging Face) to build reasoning systems and agentic workflows for asset and work intelligence
• Ensure ML infrastructure meets production standards for latency, reliability, explainability, and security