AI Engineer

Machine Learning Deployment and GovernanceDevelops and refines ML deployment pipelines and workflows to support production-grade machine learning lifecycle governance and management.Implements lifecycle governance standards for versioning and reproducibility.Optimizes workflows for scalability and reliability.Model Monitoring and PerformanceImplements and maintains monitoring systems to proactively assess model performance, reliability, and drift.Detects and reports model drift and anomalies.Generates dashboards and alerts for stakeholders.Cross-Functional CollaborationCollaborates with data scientists, software engineers, and DevOps teams to translate model requirements into operational solutions.Coordinates integration with APIs and application workflows.Participates in code reviews and design sessions.Automation of Retraining and DeploymentDesigns and automates retraining, validation, and deployment processes to ensure model accuracy and responsiveness.Implements checks before deployment.Schedules retraining jobs based on triggers.Data Governance and SecurityApplies healthcare data governance and security protocols to ML systems, ensuring compliance with regulatory standards.Implements access controls and encryption.Supports compliance audits and documentation.Cloud Infrastructure OptimizationConfigures and optimizes cloud infrastructure for ML workloads, balancing performance and cost-efficiency.Tunes resource allocation for cost and performance.Monitors infrastructure utilization and scaling needs.Documentation and Knowledge SharingContributes to technical documentation, process improvement, and knowledge sharing across teams.Maintains technical documentation for ML workflows.Shares best practices and lessons learned.Marginal or Periodic FunctionsAssists with proof-of-concept evaluations for new MLOps tools.Participates in post-mortem reviews for ML pipeline incidents.Supports periodic updates to model documentation and compliance records.Adheres to internal controls and reporting structure.Performs related duties as required.Knowledge/Skills/AbilitiesFunctional/Technical SkillsDemonstrates proficiency in programming and scripting for ML workflows and automation.Applies knowledge of machine learning concepts, model lifecycle, and production deployment.Possesses a working knowledge of CI/CD pipelines, cloud infrastructure, and container orchestration.Problem SolvingTroubleshoots and optimizes ML systems in dynamic, real-world environments.Exhibits strong analytical and problem-solving skills for identifying and resolving operational challenges.Initiates process improvements and adopts emerging MLOps practices.Adaptability and Continuous LearningCommits to continuous learning and professional development in machine learning operations.Adapts quickly to new tools and emerging best practices.Prototypes with unfamiliar libraries and seeks feedback.Process ManagementOrganizes ML workflows for efficiency and reliability.Automates retraining and evaluation jobs.Standardizes packaging and environments for reproducibility.Dealing with AmbiguityHandles unclear requirements through testing and iteration.Communicates trade-offs clearly and revisits decisions as new data emerges.Designs small experiments to reduce uncertainty.Peer RelationshipsCommunicates and collaborates effectively across technical and non-technical teams.Participates in reviews and stand-ups to share updates.Co-develops APIs and integration points.