Package, version, and deploy ML models into Databricks-managed execution environments.
Set up automated workflows for training, retraining, evaluation, and scheduled job execution.
Support creation and integration of machine learning models including classification, forecasting, anomaly detection, NLP, and PI models.
Enable LLM/GenAI-driven solutions by integrating
Embedding model generation
RAG architectures
Vector databases
LangChain agentic workflows
Optimize resource usage, runtime configurations, and code execution patterns for ML workloads.
Collaborate with Data Scientists to translate experimental notebooks into production-ready pipelines.
Implement platform-level controls for environment consistency, dependency management, access control, and model versioning.
Support troubleshooting, debugging, and performance improvements for ML workloads.
Document standards, templates, guidelines, and best practices for MLOps teams.
Work cross-functionally with product, engineering, and analytics teams across PI.
Required Qualifications
Bachelor’s/Master’s degree in Computer Science, Engineering, or related field
6–9 years of relevant experience in ML Engineering, MLOps, or platform engineering
Strong hands-on experience with Databricks, Spark (batch/streaming), Python, Scala
Experience enabling ML lifecycle tools such as MLflow (tracking, packaging, model registration)
Strong CI/CD experience using Git, GitHub Actions, Jenkins, or Azure DevOps
Experience deploying AI/ML models into cloud environments (Azure preferred)
Ability to create and integrate embedding models, semantic vectors, and LLM-driven components
Experience with LangChain for agentic workflows and integration of tools/functions
Strong problem-solving, debugging, and collaboration skills
Preferred Qualifications
Experience with Azure OpenAI or OpenAI-compatible LLM APIs
Familiarity with healthcare claims workflows, PI, FWA, provider billing, or pricing
Experience in Agile/Scrum environments
Strong understanding of software engineering best practices, packaging, dependency management
Good-to-Have Data Knowledge
Call Center datasets (member & provider interactions)
Provider RCM datasets (billing, coding, authorizations)
EHR/clinical datasets for cross-domain validation
If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!
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