Related skills
nlp docker python kubernetes llms📋 Description
- Own the full ML lifecycle from data ingestion to retraining.
- Transition models from prototypes to scalable production systems.
- Build and maintain CI/CD pipelines for ML models ensuring reproducibility.
- Design cloud-based infra (AWS/Azure) for training, inference, monitoring.
- Integrate LLMs, generative AI, and NLP into Clinical AI products.
- Develop scalable inference pipelines and APIs for customer-facing AI.
🎯 Requirements
- 5+ years of professional experience in software engineering or AI/ML.
- Bachelor’s or Master’s degree in CS/Engineering or related field (or equivalent).
- Strong Python or Java coding skills with software engineering best practices.
- Hands-on experience deploying and scaling ML models in production.
- Proficiency with AWS or Azure, containers, and Infrastructure as Code.
- Experience with MLflow, SageMaker, Kubeflow; CI/CD, monitoring, observability for ML.
🎁 Benefits
- Experience with clinical or healthcare AI applications.
- Familiarity with Hugging Face, PyTorch, TensorFlow, or similar.
- Exposure to agentic AI and generative AI applications.
- AWS Associate-level certification (ML Engineer or Solutions Architect).
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