Advanced Machine Learning Development
Budget: $750 – $1,500 USD
I have several software initiatives that require robust, production-ready machine-learning models. I’m looking for an AI engineer who can take a dataset from raw form through model design, training, validation, and deployment, then document the entire pipeline so it can be maintained by my in-house team.
You should be comfortable choosing the most appropriate algorithms, handling feature engineering, tuning hyper-parameters, and explaining trade-offs in accuracy, speed, and resource use. Typical tools in my stack include Python, TensorFlow, PyTorch, scikit-learn, and Docker, so experience with these will be valuable.
Deliverables
• Clean, well-commented code in a Git repo
• Reproducible training scripts and environment files
• A trained model (or models) saved in a portable format
• Evaluation report with key metrics and error analysis
• Deployment-ready API or packaged module
• Brief hand-off guide so my developers can extend the work
Acceptance criteria
• Model meets or exceeds agreed performance benchmarks on a held-out test set
• Training pipeline runs end-to-end in the provided environment without manual intervention
• Code passes automated linting/tests and follows PEP 8 standards
• Documentation is clear enough for a new engineer to replicate results in one attempt
If this aligns with your expertise in machine learning, let’s discuss data specifics and milestones to get started right away.
You should be comfortable choosing the most appropriate algorithms, handling feature engineering, tuning hyper-parameters, and explaining trade-offs in accuracy, speed, and resource use. Typical tools in my stack include Python, TensorFlow, PyTorch, scikit-learn, and Docker, so experience with these will be valuable.
Deliverables
• Clean, well-commented code in a Git repo
• Reproducible training scripts and environment files
• A trained model (or models) saved in a portable format
• Evaluation report with key metrics and error analysis
• Deployment-ready API or packaged module
• Brief hand-off guide so my developers can extend the work
Acceptance criteria
• Model meets or exceeds agreed performance benchmarks on a held-out test set
• Training pipeline runs end-to-end in the provided environment without manual intervention
• Code passes automated linting/tests and follows PEP 8 standards
• Documentation is clear enough for a new engineer to replicate results in one attempt
If this aligns with your expertise in machine learning, let’s discuss data specifics and milestones to get started right away.