ML Model Development & MLOps Expert
New
United StatesContract
Salary95 - 135 USD per hour
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Job Details
- Required Skills
- DockerPythonKubernetesMLFlowPyTorchAirflowSparkTensorflowCI/CDMLOps
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
- Graduate-level study, applied ML experience, research experience, or production engineering experience is highly relevant
- Equivalent practical experience in ML engineering, AI systems, MLOps, model deployment, or technical review is also valuable
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
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