Senior ML/AI Engineer

TailorCare

6d•Remote

About The Position

TailorCare is hiring a Senior ML/AI Engineer to build and operate the ML/AI systems that guide patient outreach and care decisions. The role takes models from prototype to production and owns the engineering behind predictive and decisioning systems, including deployment and retraining workflows, monitoring, real-time event-driven integrations, and production GenAI applications used by care teams. The engineer will help grow the platform from a Databricks lakehouse toward a hybrid AWS architecture, while improving reliability, cost controls, and engineering standards. The position reports to the Director, AI & Decision Intelligence and collaborates with data engineering, software engineering, clinical, and operations partners. TailorCare is remote-first, with a hybrid office option in Nashville; travel up to 10% may be required for onsite meetings, collaboration, and company events.

Requirements

Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent experience.
5+ years in ML engineering, MLOps, or data science, including 3+ years operating ML systems in production.
Production experience with Databricks, PySpark, Delta Lake, and MLflow.
Production experience with AWS data and ML services and infrastructure as code, such as Terraform, CDK, or CloudFormation.
Hands-on experience designing and operating event-driven or streaming systems such as Kinesis, Kafka, or equivalent, with schema or contract management.
Strong Python and SQL skills, and experience building and evaluating ML models with scikit-learn, XGBoost, or PyTorch.
Experience with CI/CD for ML workloads and model and pipeline monitoring.
Ability and willingness to travel up to 10% as needed for onsite meetings, team collaboration, and company events.

Nice To Haves

Experience building production recommendation, decisioning, or experimentation systems, including uplift or bandit-style approaches.
Experience with dbt and lakehouse data modeling.
Experience with healthcare data such as claims, eligibility, prior authorization, or EHR, and PHI handling requirements.
Experience with containerized model serving and staged or canary deployment patterns.
Experience with Amazon Bedrock or other managed foundation-model platforms under a HIPAA BAA.
Experience with agentic frameworks and retrieval systems in production.
Experience building or operating LLM-based applications, including evaluation and guardrails.

Responsibilities

Build the decisioning and feedback layer between model scores and actions, including action selection, holdout and experiment assignment, outcome capture, and measurement pipelines.
Design and build event-driven, real-time integrations between model outputs and systems that act on them, including streaming pipelines, versioned schemas and contracts, replay, ordering, and failure handling.
Instrument and manage platform cost across compute, streaming, inference, and LLM use; set budgets and alerts and make cost a release criterion alongside latency and accuracy.
Deploy, monitor, and retrain production ML models using CI/CD, a model registry, and alerting for drift, coverage, and calibration by cohort and market.
Build and improve predictive models using healthcare claims, authorization, and operational data, and validate models under production conditions before release.
Help build out the AWS-native footprint alongside the Databricks lakehouse, including infrastructure as code, IAM and secure data handling, containerized inference, and managed foundation-model services under PHI controls.
Extend the feature store and data assets to support real-time features as new event sources arrive.
Own and extend production GenAI applications, including LLM pipelines and agentic workflows, with evaluation, retrieval, guardrail, and cost controls for safe use on patient data.
Partner with data scientists to productionize models and with data and software engineering on shared pipelines, interfaces, and contracts.
Establish and uphold engineering standards with the ML engineering lead, including code review, automated testing, reproducibility, documentation, and release discipline.
Mentor data scientists on production practices.
Perform other duties as assigned.

Benefits

Paid time off and holiday plans.
Paid parental leave.
Medical insurance.
Dental insurance.
Vision insurance.
Life insurance.
Disability insurance.
Wellness resources.
Employer HSA contribution.
401k plan with employer matching.
Remote-first work environment with an option for a hybrid work model in Nashville.
Flexible work-life balance, autonomy, and an inclusive, collaborative work environment.