Machine Learning Ops Engineer

Bachelor's degree in Computer Science, Engineering, or related fields OR equivalent experience. 3+ years of industry experience. Proficiency in Kubernetes and PyTorch. Advanced Python programming skills, including data science libraries. Deep learning experience, including model implementation and training. MLOps expertise, including experiment tracking and model registries. Data engineering capabilities, including data processing pipelines. Infrastructure management experience with cloud services (AWS/Azure) and containerization (Docker). Testing and quality assurance experience for ML systems. Proven ability to thrive in startup environments with high autonomy. Strong technical communication skills. Experience with machine learning in computer vision, NLP, recommender systems, or scientific applications. Strong background in probability, machine learning, and data science. 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.

Develop code for core internal tools, including data preprocessing, metrics calculation, and visualization. Build and maintain a custom end-to-end MLOps platform. Develop production-grade QA/QC systems for deployed AI models. Create specialized tools for mining data processing and visualization. Refactor and productionize client services code. 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.