[Remote] Senior ML/AI Engineer

Note: The job is a remote job and is open to candidates in USA. TailorCare is hiring a Senior ML/AI Engineer to take machine learning models from prototype to production and operate predictive, decisioning, and GenAI systems. The role owns model deployment and monitoring, real-time integrations, platform development, and collaboration with data science, engineering, clinical, and operations teams.

Responsibilities

• Build the decisioning and feedback layer between score and action: action selection logic, holdout and experiment assignment, outcome capture, and the pipelines that measure lift per action and channel
• Design and build event-driven, real-time integrations between model output and the systems that act on it: streaming pipelines, versioned schemas and contracts, replay, ordering, and failure handling
• Own cost-to-serve across the platform: instrument compute, streaming, inference, and LLM spend per model and per decision, set budgets and alerts, and make cost a release criterion alongside latency and accuracy
• Deploy, monitor, and retrain production ML models with CI/CD, model registry, and alerting that meet a healthcare reliability bar, including drift, coverage, and calibration by cohort and market
• Build and improve predictive models on healthcare claims, authorization, and operational data, and validate them in production conditions before release
• Drive the build-out of our AWS-native footprint alongside our Databricks lakehouse: infrastructure as code, IAM and secure data handling, containerized inference, and managed foundation-model services under PHI controls
• Extend our feature store and data assets toward real-time features as new event sources arrive
• Own and extend our production GenAI applications, including LLM pipelines and agentic workflows, with the evaluation, retrieval, guardrail, and cost controls needed to run them safely on patient data
• Partner with data scientists to productionize new models, and with data and software engineering on shared pipelines, interfaces, and contracts
• Help establish and uphold engineering standards with the ML engineering lead: code review, automated testing, reproducibility, documentation, and release discipline. Mentor data scientists on production practice
• Other duties as assigned

Skills

• 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 (Terraform, CDK, or CloudFormation)
• Hands-on experience designing and running event-driven or streaming systems (Kinesis, Kafka, or equivalent) with schema or contract management
• Strong Python and SQL, and experience building and evaluating ML models with scikit-learn, XGBoost, or PyTorch
• CI/CD for ML workloads and model and pipeline monitoring
• Experience building recommendation, decisioning, or experimentation systems in production, including uplift or bandit-style approaches
• Dbt and lakehouse data modeling
• Healthcare data (claims, eligibility, prior authorization, EHR) and PHI handling requirements
• Containerized model serving and staged or canary deployment patterns
• Amazon Bedrock or other managed foundation-model platforms under a HIPAA BAA
• Agentic frameworks and retrieval systems in production
• Experience building or operating LLM-based applications, including evaluation and guardrails
• Engineering Mentality – Finds the common denominator across work and builds the platform that makes the next model faster to ship than the last
• Scientific Approach – Starts from an open question, designs the analysis or model to answer it, and validates before trusting it
• Seek Simplicity – Builds with the minimum necessary complexity and designs for the person who maintains it next
• Ownership – Takes a system from design to production to monitoring without waiting for another team to carry it
• Collaborative – Works across data engineering, software engineering, clinical, and operations to agree on interfaces and hold to them
• Good Communication – Explains what a system does and why it matters in terms a care team or finance partner can act on

Benefits

• Remote-first company with options for a hybrid work model in Nashville.
• Generous paid time off (PTO) and holiday plans.
• Paid parental leave.
• Flexibility and autonomy.
• From Day 1: medical, dental, vision, life, and disability insurance.
• Wellness resources.
• Employer HSA contribution.
• 401k plan that includes employer matching.

Company Overview