Senior Software Engineer (MLOps)

TL;DR

Senior Software Engineer (MLOps): Building and maintaining end-to-end backend infrastructure and data pipelines for training and running AI and ML models in production with an accent on cloud infrastructure, large language models, and data engineering. Focus on designing scalable, efficient systems for model training, inference, monitoring, and alerting in a distributed environment.

Company/Компания

hirify.global is a profitable tech startup focused on merging human creativity with AI to transform digital note-taking and education.

What you will do/Что делать

• Build and maintain backend infrastructure for training and running ML models in production
• Design and implement data pipelines for production data collection and model training
• Develop and maintain compute infrastructure for training and inference
• Monitor production systems and set up alerting mechanisms
• Collaborate with cross-functional teams including ML engineers, iOS engineers, QAs, and designers

Requirements/Требования

• 7+ years of software engineering experience, including data engineering
• Experience designing and building data or training pipelines
• Experience with cloud infrastructure supporting ML operations
• Experience with LLM backend design, monitoring, and optimization at scale
• Proficiency in Python and understanding of ML ecosystem (Pytorch, ONNX, TensorRT)
• Excellent problem-solving and communication skills

Nice to have/Хорошо, если есть

• Experience working in fast-paced, collaborative environments

Culture & Benefits/Культура и преимущества

• Meaningful equity in a profitable startup
• Budget for home office setup, personal development, and wellness
• Sponsored visits to Hong Kong or London office every 2 years
• Company-wide annual offsite events
• Flexible working hours and location
• Medical insurance for employee and dependents

Hiring process/Процесс найма

• Introductory call with talent acquisition
• Take-home assignment to demonstrate Ops skills
• Technical interview with ML Ops and Data Ops focus
• Hiring manager interview for growth and development discussion
• Panel interview assessing ownership and collaboration