Machine Learning Engineer

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. ResponsibilitiesAnalyze large, complex healthcare datasets to generate insights and model patient, clinical and operational patternsBuild, 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 solutionsUse experimentation frameworks to compare model performance and validate outcomesML Engineering & Operations (MLOps) Design and maintain end-to-end ML pipelines — from data ingestion to deployment and monitoringPackage models into production-grade APIs and microservices, ensuring scalability and performanceImplement CI/CD pipelines, version control and model lifecycle management using tools like MLflow, Azure DevOps, DatabricksMonitor deployed models for drift, latency and accuracy and automate retraining workflows where necessaryLeverage containerization and orchestration (Docker, Kubernetes, AKS) to deploy models in real-world environmentsEnsure governance, compliance and auditability of all deployed AI systems in line with HIPAA, GDPR and healthcare standards Requirements5+ years of hands-on experience in machine learning, data science or ML engineeringStrong 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 KubeflowExperience with cloud platforms (Azure preferred), including DevOps tooling and infrastructure automationFamiliarity with LLMOps, prompt engineering or frameworks such as LangChain, LlamaIndex is a plusDeep understanding of healthcare data and related compliance constraintsExperience building and deploying real-time or batch inference systems using robust APIsStrong communication skills and the ability to work cross-functionally with stakeholders, clinicians and engineers Nice to haveBackground in bioinformatics, digital health or clinical data modelingExperience with feature stores, streaming pipelines or event-driven ML architecturesFamiliarity with model explainability tools (e.g., SHAP, LIME) and ethical AI practicesUnderstanding of healthcare-specific data formats and standards (e.g., HL7, FHIR) We offerEnd of service gratuityPrivate healthcare and life insuranceEmployee assistance programWellness programAnnual air travel tickets for expatriatesRegular performance feedback and salary reviewsGlobal travel medical and accident insuranceReferral bonusesLearning and development opportunities including in-house training and coaching, professional certifications, over 22,000 courses on LinkedIn Learning Solutions and much more*All benefits and perks are subject to certain eligibility requirements