MLOps Engineer | Remote

CodeGeniusRecruit; United States; On-site; USD 234,000 - 351,000; Full time

Job summary

CodeGeniusRecruit is seeking an experienced ML engineer for sprint-based projects. You will complete and evaluate complex ML tasks using coding agents, reviewing model-generated implementations across training, inference, MLOps, and LLM applications.

The role requires strong experience in building production ML systems and deploying them to production environments. You will identify issues and compare model outputs to guide decisions.

Qualifications

• Strong experience in machine learning engineering.
• Experience building production ML systems and deployment infrastructure.
• Experience with LLM applications and intelligent software products.
• Ability to evaluate model-generated ML implementations and tradeoffs.
• Experience deploying ML systems to production environments.

Responsibilities

• Complete and evaluate complex ML engineering tasks using coding agents.
• Review model-generated implementations involving training, inference systems, MLOps, and LLM applications.
• Identify bugs, edge cases, performance issues, and system failure modes.
• Compare outputs from multiple models and assess strengths and weaknesses.
• Apply engineering judgment to real-world ML engineering scenarios.

Skills

ML engineering

Production ML systems

Model deployment

LLM applications

Coding agents

Tools

MLOps tools

LLM platforms

Job description

Commitment: Sprint-based project running in 12 24 hour stretches based on project requirements

Commission: $85/hour (Typical accepted tasks pay approximately $400 and take 2 3 hours after ramp-up).

What You'll Be Doing

• Complete and evaluate complex machine learning engineering tasks using coding agents.
• Review model-generated implementations involving model training, inference systems, MLOps, and LLM applications.
• Identify bugs, edge cases, performance issues, and system failure modes.
• Compare outputs from multiple models and assess strengths and weaknesses.
• Apply engineering judgment to real-world machine learning engineering scenarios.

What We're Looking For

• Strong experience in machine learning engineering.
• Strong experience building production ML systems, model deployment infrastructure, LLM applications, or intelligent software products.
• Strong experience using coding agents and developer productivity tools.
• Ability to evaluate model-generated machine learning implementations and technical tradeoffs.
• Strong experience deploying machine learning systems to production environments.