Applied Machine Learning Engineer - ML & AI Systems

Essential Responsibilities

Machine Learning Development & Modeling (Primary Focus)

• Design, develop, and deploy predictive models and ML algorithms to address business challenges such as: Schedule Optimization, Propensity Segmentation, Demand Forecasting, and Pricing • Conduct experimentation, feature engineering, and hyperparameter tuning to improve model performance in collaboration with data scientists • Implement advanced modeling techniques including tree-based methods, deep learning, and optimization algorithms. • Translate business requirements into scalable ML solutions in partnership with cross-functional stakeholders. • Take ownership of model performance from experimentation through production monitoring.

MLOps & Production Engineering (Core Expectation)

• Build scalable, secure, production-grade ML pipelines using modern cloud-native technologies. • Contribute to implementing distributed training workflows, batch and real-time inference systems, and low latency serving architectures • Deploy models via APIs and integrate with enterprise applications. • Leverage Google Cloud Platform (GCP), including Vertex AI, BigQuery, Kubernetes, Cloud Run, Dataflow, and Pub/Sub. • Implement CI/CD workflows for ML lifecycle management within defined architecture • Ensure reproducibility, versioning, and governance of models and features.

Monitoring, Reliability & System Optimization

• Define and track key performance metrics for deployed models. • Implement monitoring frameworks (e.g., Vertex AI Model Monitoring, logging, drift detection). • Analyze model behavior in production and proactively improve reliability and performance. • Collaborate with platform and infrastructure teams to ensure models meet scalability and compliance requirements.

Emerging AI Capabilities (Growth Area)

• Contribute to the development of LLM-powered solutions where appropriate (e.g., knowledge retrieval, decision-support copilots). • Support implementation of Retrieval-Augmented Generation (RAG) pipelines using BigQuery and Vertex AI. • Assist in experimentation with agent-based workflows and modern orchestration frameworks as part of TAG’s evolving AI initiatives. • Stay current with advancements in Generative AI and evaluate their practical application to enterprise healthcare use cases.

Collaboration & Mentorship

• Work cross-functionally with Product, Platform, Data and Software Engineering teams • Create clear documentation for models, pipelines, and systems. • Promote engineering best practices across data science and analytics teams. • Contribute to continuous improvement of TAG’s ML platform capabilities.