AI ML Engineer

About This Role

This engineering role involves the design, development, and deployment of artificial intelligence and machine learning solutions to improve health outcomes. It suits an engineer who can work with modern machine learning frameworks and contribute to responsible AI practices.

Responsibilities

• Assist in the design, development, testing, and deployment of AI/ML solutions using Python and modern machine learning frameworks
• Support data preparation, exploratory data analysis (EDA), feature engineering, model training, and validation activities
• Develop and maintain machine learning models using tools such as Scikit-Learn, TensorFlow, or PyTorch
• Collaborate with data engineers and data scientists to build scalable data pipelines and AI-enabled applications
• Participate in model performance monitoring, drift analysis, and continuous improvement of deployed models
• Support implementation of ML workflows, experiment tracking, and model documentation to ensure reproducibility and maintainability
• Work with cross-functional teams to understand business requirements and translate them into AI/ML solutions
• Contribute to the adoption of Responsible AI practices, ensuring fairness, reliability, explainability, and data privacy
• Create and maintain technical documentation, project artifacts, and knowledge-sharing materials
• Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives

Requirements

• Full time graduation degree
• Proven ability to lead the design, development, and implementation of scalable AI/ML solutions, leveraging Python, SQL, pandas, numpy, and modern ML frameworks (TensorFlow, PyTorch, Scikit Learn)
• Proven ability to oversee end to end ML workflows, from data preparation, feature engineering, EDA, and model development to evaluation, deployment, and continuous monitoring
• Proven ability to guide model selection, optimization, and tuning, ensuring solutions balance performance, explainability, cost, and Responsible AI principles
• Proven ability to build and maintain production grade ML pipelines with orchestration and experiment tracking tools such as MLflow, Feature Stores, ONNX, and vector databases
• Proven ability to lead drift analysis, observability, and model health monitoring to ensure ongoing accuracy, reliability, and robustness in production environments
• Proven ability to design and support scalable, multi cloud ML infrastructure (Azure/GCP primary, AWS optional), incorporating best practices for compute, storage, networking, and distributed data processing
• Proven ability to collaborate with data engineering teams to integrate real time and batch pipelines built on Synapse/BigQuery, Dataflow/Databricks, and Kafka/EventHub/PubSub