MLOps Engineer 9Core ML + MLOps

About this role

It's fun to work in a company where people truly BELIEVE in what they are doing!

*We're committed to bringing passion and customer focus to the business.*

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
...