Ai Ml Engineer

Key Responsibilities:

• Design, develop, and implement machine learning models and algorithms to solve complex healthcare problems.
• Design and implement AI/ML solutions using Python, along with libraries like pandas, NumPy, and SQL for data handling
• Build and deploy models using frameworks such as Scikit-learn, TensorFlow, and PyTorch
• Perform exploratory data analysis (EDA), statistical analysis, and feature engineering to extract meaningful insights from data
• Develop, tune, and evaluate machine learning models for both predictive and prescriptive analytics
• Conduct drift analysis to monitor model performance and maintain accuracy over time
• Collaborate with cross-functional teams to translate business requirements into scalable technical solutions
• Manage the full lifecycle of AI/ML projects, including data preparation, model development, optimization, and deployment
• Ensure scalability, reliability, and efficiency of AI/ML systems in production environments
• Stay up-to-date with the latest advancements in AI/ML tools and techniques, identifying opportunities to enhance existing solutions
• Document methodologies, findings, and insights clearly for both technical and non-technical stakeholders
• Participate actively in the full AI lifecycle: contributing to brainstorming and conceptualization, rapid prototyping and experimentation, and writing high-quality, robust production code
• Utilize and contribute to reusable AI/ML platforms, components, and frameworks, helping to improve engineering efficiency
• Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so

Key Skills:

• Strong foundation in machine learning, deep learning, and model evaluation techniques
• Proficient in Python, with hands-on experience using PyTorch, TensorFlow, and scikit-learn
• Skilled in data preprocessing, feature engineering, and SQL-based data handling
• Familiar with deploying APIs using Docker, FastAPI, and managing version control with Git
• Experience working with cloud platforms such as AWS, GCP, and Azure, including scalable ML model deployment
• Understanding of MLOps tools like MLflow and Kubeflow, and implementing CI/CD workflows for ML systems. Strong experience with Docker and Kubernetes (AKS) for cloud-native development.
• Experience with message queues like Azure Service Bus.
• In-depth knowledge of LLM vulnerabilities and implementation of guardrails.
• Hands-on experience with FastAPI and Pydantic for building microservices.
• Proficiency with explainable AI tools like LIME and SHAP.