MLOps Engineer

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Job Description

EL3 – Databricks MLOps Engineer (Contract) Domain: 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

The 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.

The 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.

Key Responsibilities

• Enable and automate the end-to-end ML lifecycle on Databricks (environment setup, model workflow automation, job scheduling, monitoring hooks).
• Build frameworks, templates, and utilities that make ML development and experimentation reproducible and scalable.
• Implement CI/CD pipelines using Git, GitHub Actions, Jenkins, Azure DevOps, or similar tools.
• 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!

Hiring Related Queries

India: HiringsupportIndia@fractal.ai

Outside India: HiringsupportROW@fractal.ai

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