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