MLOps Engineer
Job Description
It's fun to work in a company where people truly BELIEVE in what they are doing!
Job Description\n\nEL3 – Databricks MLOps Engineer (Contract)
\n\nDomain: Claims Payment Integrity | M&R, C&S, E&I Claims (preferred)
Actuarial & Forecasting Analytics Exposure is an Added Advantage
Tech Stack: Databricks, Spark, Python, Scala, Azure, GitHub Actions, Terraform
AI/LLM Capabilities: Embedding Models, LLM Integration, LangChain Agentic Frameworks
Role Summary
\n\nThe EL3 Databricks MLOps Engineer is a senior hands-on role responsible for enabling end-to-end machine learning lifecycle automation on Databricks. This includes building and maintaining the CI/CD infrastructure, environment configuration, packaging and deploying ML models, supporting reproducible experiments, and ensuring scalable job orchestration for AI/ML workloads, including LLM-based applications.
\nThe role partners closely with Data Scientists, AI/ML Engineers, platform teams, and business stakeholders within Claims Payment Integrity to ensure robust, reliable, and automated ML delivery.
\n\nKey Responsibilities
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Enable and automate the end-to-end ML lifecycle on Databricks (environment setup, model workflow automation, job scheduling, monitoring hooks).
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Build frameworks, templates, and utilities that make ML development and experimentation reproducible and scalable.
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Implement CI/CD pipelines using Git, GitHub Actions, Jenkins, Azure DevOps, or similar tools.
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Package, version, and deploy ML models into Databricks-managed execution environments.
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Set up automated workflows for training, retraining, evaluation, and scheduled job execution.
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Support creation and integration of machine learning models including classification, forecasting, anomaly detection, NLP, and PI models.
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Enable LLM/GenAI-driven solutions by integrating:
\nEmbedding model generation
\nRAG architectures
\nVector databases
\nLangChain agentic workflows
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Optimize resource usage, runtime configurations, and code execution patterns for ML workloads.
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Collaborate with Data Scientists to translate experimental notebooks into production-ready pipelines.
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Implement platform-level controls for environment consistency, dependency management, access control, and model versioning.
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Support troubleshooting, debugging, and performance improvements for ML workloads.
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Document standards, templates, guidelines, and best practices for MLOps teams.
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Work cross-functionally with product, engineering, and analytics teams across PI.
\n\n - \n
Bachelor’s/Master’s degree in Computer Science, Engineering, or related field
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6–9 years of relevant experience in ML Engineering, MLOps, or platform engineering
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Strong hands-on experience with Databricks , Spark (batch/streaming), Python, Scala
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Experience enabling ML lifecycle tools such as MLflow (tracking, packaging, model registration)
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Strong CI/CD experience using Git, GitHub Actions, Jenkins, or Azure DevOps
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Experience deploying AI/ML models into cloud environments (Azure preferred)
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Ability to create and integrate embedding models , semantic vectors, and LLM-driven components
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Experience with LangChain for agentic workflows and integration of tools/functions
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Strong problem-solving, debugging, and collaboration skills
\n\n - \n
Experience with Azure OpenAI or OpenAI-compatible LLM APIs
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Familiarity with healthcare claims workflows, PI, FWA, provider billing, or pricing
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Experience in Agile/Scrum environments
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Strong understanding of software engineering best practices, packaging, dependency management
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Call Center datasets (member & provider interactions)
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Provider RCM datasets (billing, coding, authorizations)
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EHR/clinical datasets for cross-domain validation
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Required Qualifications
\n\nPreferred Qualifications
\n\nGood-to-Have Data Knowledge
\n\nIf you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!
\n\nHiring Related Queries
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