Design, build, and deploy end-to-end machine learning solutions across structured (tabular) medical datasets and unstructured/non-tabular data (text, images, OCR, signals).
Apply advanced ML, deep learning, and LLM-based approaches to solve real-world healthcare/medical challenges.
Collaborate with cross-functional teams (engineering, product, domain experts) to translate business requirements into scalable data science solutions.
Drive MLOps best practices: experiment tracking, model versioning, CI/CD, monitoring, and automated retraining pipelines.
Ensure production-grade robustness using
Test-Driven Development (TDD)
and
OOP principles
in ML pipelines.
Contribute to architecture discussions and mentor junior team members.

Technical Skills & Requirements

Strong proficiency in
Machine Learning, Deep Learning, and Statistical Modeling.
Expertise in handling
tabular medical data
(EHRs, lab reports, prescriptions) and
non-tabular data
(text, imaging, audio).
Experience with
LLMs
(fine-tuning, prompting, integration with downstream tasks).
Hands-on experience with
MLOps frameworks and practices
:
MLflow, DVC, Airflow, Kubeflow, Weights & Biases
Model deployment and monitoring in production
Strong software engineering skills:
Python, SQL, TDD, OOP, design patterns.
Containerization & orchestration:
Docker, Kubernetes (nice-to-have)
.
Familiarity with cloud platforms (
AWS / GCP / Azure
) for scalable ML solutions.
Knowledge of medical data compliance standards (HIPAA, GDPR) is a plus.

Preferred Frameworks/Tools

ML/Deep Learning:
scikit-learn, PyTorch, TensorFlow, Hugging Face, XGBoost, LightGBM
NLP/LLMs:
Transformers, LangChain, RAG pipelines
MLOps:
MLflow, DVC, Airflow, Kubeflow
Data Processing:
pandas, PySpark, SQL, Polars

Soft Skills

Strong problem-solving and analytical mindset.
Ability to explain technical concepts to non-technical stakeholders.
Leadership and mentorship capabilities.

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