Lead ML/AI Engineer

TailorCare

. Own the decisioning and feedback layer between model score and action, including action selection and ranking, holdout and experiment assignment, outcome capture, production ROI calculation, and lift-per-action and channel measurement pipelines .

Posted 11/4/2026full-timeRemote • Tennessee • United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in ML Engineering and MLOps, with a strong focus on production systems, event-driven architectures, and cost management. Proficient in AWS services, Databricks, and CI/CD practices for ML workloads, while ensuring compliance with healthcare data handling standards.

Highest-signal resume keywords

ML EngineeringAWS Data ServicesEvent-Driven SystemsCI/CD for ML WorkloadsProduction ML Systems

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonSQLTerraformDatabricksPySparkMLflowKinesisKafkaCI/CDUplift Modeling

Soft Skills

MentorshipCollaborationTechnical Leadership

Tools & Technologies

AWSDatabricks LakehouseContainerized InferenceFoundation-Model PlatformsAgentic Frameworks

Industry Keywords

Healthcare EconomicsValue-Based CarePHI HandlingDecisioning SystemsExperimentation Platforms

Tech Stack

Tools & technologies

AWSKafkaPySparkPythonPyTorchScikit-LearnSQLTerraform

About the role

Key responsibilities & impact

Own the decisioning and feedback layer between model score and action, including action selection and ranking, holdout and experiment assignment, outcome capture, production ROI calculation, and lift-per-action and channel measurement pipelines
Own the engineering implementation, auditability, and reproducibility of the production savings calculation
Design event-driven, real-time integrations between model output and action systems, including streaming pipelines, versioned schemas and contracts, replay, ordering, idempotency, and failure handling
Own contracts with partner teams
Define per-model and per-decision budgets for compute, streaming, inference, and LLM spend
Make cost a release criterion alongside latency and accuracy
Define the production bar for ML models, including CI/CD, model registry, alerting, drift, coverage, and cohort, market, and payer calibration
Engineer model and ranking promotion gates before production release
Own target architecture and technical roadmap across AWS-native services and the Databricks lakehouse
Lead infrastructure as code, IAM, secure data handling, containerized inference, and managed foundation-model services under PHI controls
Set technical direction and production standards for GenAI applications, LLM pipelines, and agentic workflows
Establish evaluation, retrieval, guardrail, and cost controls for patient-data applications
Partner with data science, medical economics, data engineering, software engineering, clinical, and operations teams
Set team engineering standards for code review, automated testing, reproducibility, documentation, and release discipline
Lead design reviews, document architecture decisions, and mentor engineers and data scientists
Represent the team's technical position with R&D leadership, finance, clinical, and operations partners
Perform other duties as assigned

Requirements

What you’ll need

Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent experience
8+ years in ML engineering, MLOps, or software engineering
4+ years operating ML systems in production
2+ years as the technical lead or architect of a production ML or decisioning system
Experience building or operating prescriptive production systems such as recommendation, decisioning, prioritization, or experimentation platforms
Production experience with AWS data and ML services and infrastructure as code, including Terraform, CDK, or CloudFormation
Production experience with Databricks, PySpark, Delta Lake, MLflow, or equivalent
Hands-on experience designing and running event-driven or streaming systems such as Kinesis or Kafka with schema or contract management
Strong Python and SQL
Hands-on experience running ML workloads in production, including training pipelines, model serving, feature systems, or ML platform tooling
CI/CD for ML workloads and model and pipeline monitoring, including calibration across heterogeneous populations
Track record of setting engineering standards and raising the bar on a team through review and mentorship
Ability and willingness to travel up to 10% for onsite meetings, team collaboration, and company events
Preferred: scikit-learn, XGBoost, or PyTorch
Preferred: healthcare economics or value-based care exposure
Preferred: dbt and lakehouse data modeling
Preferred: healthcare data and PHI handling requirements
Preferred: uplift modeling, bandit or sequential decisioning, or holdout-based measurement
Preferred: containerized model serving and staged or canary deployment patterns
Preferred: Amazon Bedrock or other managed foundation-model platforms under a HIPAA BAA
Preferred: agentic frameworks and retrieval systems
Preferred: LLM-based applications, including evaluation and guardrails

Benefits

Comp & perks

Remote-first company with options for a hybrid work model in Nashville
Generous paid time off (PTO) and holiday plans
Paid parental leave
Flexible and autonomous work-life balance support
Medical, dental, vision, life, and disability insurance from Day 1
Wellness resources
Employer HSA contribution
401k plan with employer matching
Inclusive work environment
Meaningful work supporting patients and the company mission