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MLOps Engineer (Databricks/AWS)

Inabia Software & Consulting Inc.Texas City, TX🇺🇸United StatesPosted 18 Jul 2026

Quick Overview

Work Type
Hybrid
Level
Mid Senior

Job Description

Required Skills
  • Strong hands-on experience with Databricks Machine Learning
  • Proficiency in Python, PySpark, and SQL for developing and operationalizing machine learning solutions.
  • Hands-on experience with MLflow for experiment tracking, model registry, model versioning, and model lifecycle management.
  • Experience deploying and managing machine learning models using Databricks Model Serving and batch inference pipelines.
  • Strong understanding of the end-to-end ML lifecycle, including feature engineering, model training, validation, deployment, monitoring, and retraining.
  • Experience building scalable ML pipelines using Databricks Workflows and Delta Lake.
  • Hands-on experience with AWS services such as S3, IAM, EC2, Lambda, ECR, ECS/EKS, CloudWatch, and Secrets Manager.
  • Experience implementing CI/CD pipelines for Databricks and ML workloads using Git, Bitbucket, Jenkins, and Databricks Asset Bundles (DAB).
  • Experience with infrastructure automation using Terraform (Infrastructure as Code).
  • Strong understanding of Apache Spark architecture, optimization, and distributed data processing.
  • Experience working with Unity Catalog for governance, security, and access management.
  • Knowledge of model monitoring, data drift detection, model performance monitoring, and automated retraining strategies.
  • Understanding of MLOps best practices, including reproducibility, versioning, testing, and governance.
  • Strong collaboration skills to work with Data Scientists, Data Engineers, and Platform Engineering teams.
  • Excellent analytical, problem-solving, and communication skills.
Roles & Responsibilities
  • Design, develop, and maintain end-to-end MLOps pipelines on Databricks running on AWS.
  • Build and automate machine learning workflows covering data preparation, feature engineering, model training, evaluation, deployment, and monitoring.
  • Deploy and manage ML models using MLflow Model Registry and Databricks Model Serving.
  • Develop and maintain CI/CD pipelines for ML solutions across development, staging, and production environments.
  • Collaborate with Data Scientists to productionize machine learning models and ensure reliable deployments.
  • Monitor model health, prediction quality, data drift, and system performance, and implement retraining strategies where required.
  • Optimize Databricks workloads for performance, scalability, and cost efficiency.
  • Implement Infrastructure as Code (Terraform) for provisioning and managing Databricks and AWS resources.
  • Ensure platform security, governance, and compliance using Unity Catalog and AWS IAM.
  • Troubleshoot production issues, perform root cause analysis, and continuously improve platform reliability.
  • Document MLOps processes, deployment standards, and operational best practices.

Skills

Unity

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