Remote | ML Model Development & MLOps Expert — $95–$135/hour

24-Mag • New York, NY

19h • Remote

About The Position

We are sharing a specialised part-time consulting opportunity for professionals experienced in machine learning engineering, model development, Python, ML frameworks, model deployment, MLOps, and structured AI workflow review. This role supports current and upcoming remote consulting opportunities focused on machine learning model evaluation, ML engineering workflow review, model deployment assessment, MLOps documentation, technical task development, and high-quality project execution. Selected professionals will apply their machine learning engineering expertise to review realistic ML scenarios, evaluate technical outputs, prepare structured written feedback, and support accurate, evidence-based AI engineering workflow tasks.

Requirements

• Professional experience in machine learning engineering, applied ML, data science engineering, AI engineering, MLOps, model deployment, or related technical roles.
• Background in one or more areas such as model development, Python, PyTorch, TensorFlow, data pipelines, model evaluation, production ML, or ML infrastructure.
• Familiarity with workflows involving training, validation, experiment tracking, model serving, monitoring, deployment, and technical documentation.
• Comfort reading and preparing ML artifacts such as notebooks, model reports, experiment logs, pipeline documentation, deployment notes, and technical summaries.
• Strong written communication skills.
• Ability to work independently in a remote, project-based environment.
• A degree or professional background in computer science, machine learning, data science, statistics, mathematics, software engineering, computer engineering, or a related technical field is helpful.
• Equivalent practical experience in ML engineering, AI systems, MLOps, model deployment, or technical review is also valuable.

Nice To Haves

• Experience with PyTorch, TensorFlow, scikit-learn, Python, SQL, Docker, Kubernetes, cloud platforms, MLflow, Weights & Biases, Airflow, Spark, or similar tools.
• Familiarity with model deployment, inference optimization, monitoring, feature stores, data validation, experiment tracking, or production ML systems.
• Experience preparing or reviewing technical documentation, model cards, evaluation reports, deployment plans, pipeline notes, or ML system designs.
• Background in AI labs, applied ML teams, SaaS platforms, data infrastructure, research engineering, or high-scale production environments.
• Strong attention to detail in technical, data-heavy, and model-driven workflows.
• Graduate-level study, applied ML experience, research experience, or production engineering experience is highly relevant.

Responsibilities

• Review machine learning scenarios involving model development, training workflows, feature engineering, evaluation metrics, and model behavior.
• Evaluate ML outputs against source materials, technical requirements, model assumptions, and documented review criteria.
• Support structured review of model architectures, experiment notes, training pipelines, evaluation reports, and technical explanations.
• Identify missing assumptions, implementation gaps, metric issues, and expected ML review outcomes.
• Review materials involving Python, PyTorch, TensorFlow, data preprocessing, model experimentation, inference workflows, and ML code-adjacent tasks.
• Evaluate technical recommendations for clarity, correctness, feasibility, reproducibility, and alignment with ML engineering standards.
• Support structured review of notebooks, model documentation, pipeline notes, experiment summaries, and implementation plans.
• Prepare clear written feedback based on source materials and verifiable technical criteria.
• Review scenarios involving model deployment, monitoring, versioning, CI/CD, data pipelines, production ML systems, and MLOps workflows.
• Provide structured feedback on technical accuracy, workflow realism, deployment readiness, and engineering reasoning.
• Support evaluation workflows involving AI-generated ML plans, debugging notes, model analysis, and production-readiness assessments.
• Maintain accuracy, consistency, and professional judgment across submitted work.

Benefits

• Competitive hourly compensation
• Flexible scheduling
• Remote structure
• Part-time commitment
• Weekly payments
• Projects may be extended, shortened, or adjusted depending on scope and performance

What This Job Offers

• Job Type: Part-time
• Career Level: Senior
• Education Level: Associate degree