Machine Learning Ops Engineer

Job not on LinkedIn

August 8

Apply Now

Receive Emails with Similar Jobs

Stratum AI

WebsiteLinkedInAll Job Openings

Artificial Intelligence • Mining • B2B

Stratum AI is reimagining mining with its advanced AI-driven technology that creates more accurate models for mine planning and grade control through Machine Learning. Their innovations enable increased average mined grade by distinguishing between ore and waste more effectively and improving the realized net present value (NPV) of mine plans. Stratum AI's dynamic reconciliation technology tracks ore in real-time, allowing for efficient error correction and enhanced drill targeting, thereby leading to reduced waste and optimized resource extraction.

11 - 50 employees

Founded 2018

🤖 Artificial Intelligence

🤝 B2B

📋 Description

• Develop robust and well-tested code for core internal tools: • Create data preprocessing modules for mining data • Implement metrics calculations and evaluation pipelines • Build visualization tools for 3D models and ML performance metrics • Troubleshoot and fix issues in existing metrics code • Build and maintain our custom end-to-end MLOps platform: • Implement experiment tracking systems • Create model registry with versioning and storage • Develop automated testing frameworks • Build interfaces between different components of the ML pipeline • Develop production-grade QA/QC systems for deployed AI models: • Implement input data validation • Create automated alerts for performance issues • Set up monitoring for data drift • Build dashboards for model performance metrics • Create specialized tools for mining data: • Implement spatial data processing utilities • Build visualization tools for 3D geological data • Develop data converters between different mining data formats • Create utilities for coordinate transformations • Refactor and productionize code created by the client services team: • Convert notebooks into modular Python packages • Implement proper error handling and logging • Add comprehensive testing to existing code • Improve performance of data processing pipelines • Provide technical expertise to the client services team • Manage infrastructure for data processing, model training, and serving • Mentor junior engineers, perform code reviews, and write documentation • Proactively identify technical challenges and drive improvement initiatives

🎯 Requirements

• Bachelor's degree in Computer Science, Engineering, or related fields OR equivalent experience in software development and ML engineering • 3+ years of industry experience • Kubernetes, PyTorch • Advanced Python programming skills: • Proficiency with data science libraries (numpy, pandas) • Experience with visualization tools • Ability to write modular, robust, and tested Python code • Strong debugging skills for complex ML systems • Deep learning experience: • Implementation of neural network models and training workflows • Understanding of model architecture selection • Knowledge of model evaluation techniques • MLOps expertise: • Creating experiment tracking systems • Building model registries and versioning systems • Implementing model deployment pipelines • Setting up monitoring for model performance • Data engineering capabilities: • Experience with SQL and database principles • Familiarity with database frameworks • Ability to create data processing pipelines • Experience handling common mining data formats and transformations • Infrastructure management: • Experience with cloud services (AWS/Azure) • Understanding of containerization (Docker or Singularity) • Knowledge of compute resources for ML • Testing and quality assurance: • Implementing automated tests for ML systems • Creating QA/QC systems for model predictions • Designing validation steps for data inputs/outputs • Ability to write efficient software following best practices • Proven ability to thrive in startup environments with low structure and high autonomy • Strong technical communication skills and ability to collaborate in a remote team setting • Experience working with machine learning in computer vision, NLP, recommender systems, or scientific applications • Strong background in probability, machine learning, and data science • Strong experience with data analysis/processing libraries such as pandas and numpy • Excellent communication skills for both technical and non-technical audiences • Self-learner and motivated to pick up new skills