Are you passionate about both building cutting-edge AI models and bringing them to life in scalable production environments? At EPAM, we are looking for a Machine Learning Engineer with a hybrid profile in Data Science and MLOps to support a major healthcare transformation project aligned with Abu Dhabi’s 2025 digital health vision.
You will work at the intersection of data science, software engineering and cloud infrastructure to design, build, deploy and monitor AI solutions that address real-world healthcare challenges — from personalized care and automation to regulatory compliance and operational optimization.

REQUIREMENTS

• 5+ years of hands-on experience in machine learning, data science or ML engineering
• Strong background in Python, SQL and distributed processing tools (e.g., Spark)
• Proven track record with ML frameworks (e.g., Scikit-learn, TensorFlow, PyTorch, MLlib)
• Proficiency in MLOps tools such as MLflow, DVC, Azure ML, SageMaker or Kubeflow
• Experience with cloud platforms (Azure preferred), including DevOps tooling and infrastructure automation
• Familiarity with LLMOps, prompt engineering or frameworks such as LangChain, LlamaIndex is a plus
• Deep understanding of healthcare data and related compliance constraints
• Experience building and deploying real-time or batch inference systems using robust APIs
• Strong communication skills and the ability to work cross-functionally with stakeholders, clinicians and engineers

• Analyze large, complex healthcare datasets to generate insights and model patient, clinical and operational patterns
• Build, train and evaluate machine learning models using statistical and deep learning techniques (e.g., NLP, CV, LLMs)
• Collaborate with clinicians and business stakeholders to translate domain needs into data-driven solutions
• Use experimentation frameworks to compare model performance and validate outcomes
• ML Engineering & Operations (MLOps) Design and maintain end-to-end ML pipelines — from data ingestion to deployment and monitoring
• Package models into production-grade APIs and microservices, ensuring scalability and performance
• Implement CI/CD pipelines, version control and model lifecycle management using tools like MLflow, Azure DevOps, Databricks
• Monitor deployed models for drift, latency and accuracy and automate retraining workflows where necessary
• Leverage containerization and orchestration (Docker, Kubernetes, AKS) to deploy models in real-world environments
• Ensure governance, compliance and auditability of all deployed AI systems in line with HIPAA, GDPR and healthcare standards