Talent Space, Inc. is looking for a AI/ML Engineer for one of it's clients in the healthcare domain.
Responsibilities:
- Prepare, clean, and engineer data, ensuring high quality for model input and adhering to best practices in feature engineering and data selection.
- Develop and fine-tune ML models, using best practices in model evaluation, hyperparameter tuning, and optimization to ensure accuracy and efficiency.
- Lead end to end machine learning lifecycle, from data preparation and model training to deployment and ensuring high-performance, scalable, and robust models.
- Lead the deployment of models into production environments, establish monitoring and versioning systems, and continuously optimize for performance and stability.
- Maintain comprehensive documentation of model development and deployment processes, ensuring adherence to data governance, regulatory requirements, and compliance standards.
- Provide technical mentorship to junior ML/AI engineers, promoting best practices in model development, code review, and problem-solving within a collaborative team environment.
- Partner closely with data engineering, product, and DevOps teams to align on objectives, ensure data pipelines are ML-ready, and integrate models into product features.
- Stay current with and experiment with the latest ML techniques and technologies to enhance model accuracy, efficiency, and reliability.
Required Skills:
- Master s degree in computer science, Data Science, Machine Learning, or a related field.
- 5+ years of hands-on experience in in model development, training, deployment, and lifecycle management, including supervised and unsupervised learning.
- Strong experience with ML frameworks - TensorFlow, PyTorch, scikit-learn
- Advanced programming skills in Python, Pandas and NumPy.
- Familiarity with model versioning and monitoring tools (e.g., MLflow, Kubeflow) and experience with cloud platforms (e.g., AWS, Azure, or Google Cloud Platform) for ML model deployment.
- Ability to deploy models in production, with experience in model packaging, version control, and monitoring.
- Familiarity with MLOps practices, including CI/CD pipelines, automation, and integration of ML models in production environments.
- Strong analytical skills, with the ability to design solutions for complex business and technical challenges.
- Excellent written and verbal communication skills, with the ability to convey technical concepts to both technical and non-technical stakeholders.