MLOps Engineer – Databricks/AWS | Columbus, OH / Dallas, TX / Atlanta, GA

Job Overview MLOps Engineer Dallas, TX

We are seeking an experienced MLOps Engineer with 10+ years of experience to support data engineering, machine learning operations, cloud infrastructure, and production analytics initiatives. The ideal candidate will have strong hands-on expertise with Databricks, AWS, Python, PySpark, Apache Spark, Airflow, Delta Lake, Unity Catalog, CI/CD, Git, and Terraform.

This is a contract opportunity for a senior engineering professional who can design and maintain scalable data pipelines, optimize Spark workloads, support machine learning deployments, automate CI/CD processes, and ensure reliable production operations.

The position is available in Columbus, OH; Dallas, TX; or Atlanta, GA. Local candidates are required, and candidates must be available for an onsite interview.

Position Details

Job Title: MLOps Engineer – Databricks/AWS
Experience: 10+ Years
Locations: Columbus, OH / Dallas, TX / Atlanta, GA
Job Type: Contract
Work Arrangement: Onsite
Candidate Requirement: Local Candidates Only
Interview: Onsite Interview Required
Primary Technologies: Databricks, AWS, Python, PySpark, Apache Spark, Airflow, Delta Lake, Unity Catalog, CI/CD, Git, Terraform

Key Responsibilities

• Design, develop, and maintain scalable data pipelines using Databricks, Apache Spark, PySpark, and cloud technologies.
• Build reliable data processing workflows to support analytics, machine learning, and enterprise data initiatives.
• Develop and optimize PySpark and Spark applications for large-scale data processing.
• Analyze Spark workloads and implement optimization techniques to improve performance and resource utilization.
• Work with Databricks to develop, deploy, monitor, and maintain production data and ML workloads.
• Implement and maintain data pipelines using Delta Lake and modern lakehouse architecture principles.
• Work with Unity Catalog to support data governance, access control, security, and discoverability.
• Support machine learning model deployment and operationalization within cloud and Databricks environments.
• Build automation for machine learning and data engineering workflows.
• Develop and maintain CI/CD pipelines for data, ML, and infrastructure deployments.
• Use Git and source-control best practices to manage code, configurations, and deployment processes.
• Implement Infrastructure as Code using Terraform.
• Design and manage cloud infrastructure and services using AWS.
• Build workflow orchestration solutions using Apache Airflow.
• Monitor production pipelines, applications, workflows, and ML workloads.
• Troubleshoot production failures, performance issues, data quality problems, and deployment issues.
• Implement monitoring, alerting, logging, and operational processes for production environments.
• Collaborate with Data Engineers, Data Scientists, ML Engineers, Cloud Engineers, DevOps teams, and business stakeholders.
• Participate in architecture discussions and recommend solutions for scalability, reliability, security, and maintainability.
• Create technical documentation covering pipelines, deployment processes, infrastructure, workflows, and operational procedures.

Required Technical Skills

Databricks

• Strong hands-on experience with Databricks.
• Experience developing and supporting production workloads on the Databricks platform.
• Knowledge of Databricks notebooks, jobs, clusters, workflows, and deployment practices.
• Experience working with Databricks in enterprise data and ML environments.

AWS

• Strong experience with Amazon Web Services (AWS).
• Experience designing and supporting cloud-based data and ML solutions.
• Understanding of AWS infrastructure, security, scalability, and deployment practices.
• Experience integrating Databricks workloads with AWS services.

Python & PySpark

• Strong programming experience with Python.
• Extensive experience with PySpark and distributed data processing.
• Strong knowledge of Apache Spark architecture and execution.
• Ability to troubleshoot and optimize Spark applications.

Delta Lake & Unity Catalog

• Hands-on experience with Delta Lake.
• Understanding of lakehouse architecture and transactional data processing.
• Experience with Unity Catalog for governance, permissions, data access, and management.
• Understanding of data security and governance within Databricks environments.

Airflow

• Experience developing and maintaining workflows using Apache Airflow.
• Ability to create reliable DAGs and orchestrate complex data pipelines.
• Experience monitoring and troubleshooting scheduled workflows and dependencies.

CI/CD, Git & Terraform

• Strong experience implementing CI/CD automation.
• Hands-on experience with Git and source-control workflows.
• Experience with Terraform and Infrastructure as Code.
• Ability to automate infrastructure provisioning and application deployment.
• Understanding of DevOps practices and release management.

MLOps & Machine Learning Deployment

The ideal candidate should understand the operational requirements of machine learning systems and be able to support the complete ML deployment lifecycle.

Responsibilities may include:

• Supporting model deployment into production environments.
• Automating ML deployment workflows.
• Managing dependencies between data pipelines and ML workloads.
• Implementing monitoring for production ML applications.
• Supporting model-related infrastructure and deployment processes.
• Collaborating with Data Scientists and ML Engineers to operationalize models.
• Improving reliability, scalability, and repeatability of ML workflows.

Spark Optimization

Strong Spark optimization experience is highly important for this position.

The candidate should be able to:

• Analyze Spark jobs and identify performance bottlenecks.
• Optimize PySpark transformations and actions.
• Improve partitioning and data processing strategies.
• Reduce unnecessary resource consumption.
• Troubleshoot slow-running distributed workloads.
• Design efficient pipelines for large-scale datasets.

Production Monitoring & Support

• Monitor data pipelines, ML workloads, and cloud infrastructure in production.
• Establish appropriate logging, metrics, alerts, and operational dashboards.
• Investigate production incidents and determine root causes.
• Implement permanent fixes and preventative measures.
• Support production releases and deployment activities.
• Maintain operational documentation and runbooks.

Required Qualifications

• 10+ years of professional experience in software engineering, data engineering, MLOps, cloud engineering, or related areas.
• Strong hands-on experience with Databricks and AWS.
• Advanced Python and PySpark experience.
• Strong Apache Spark knowledge.
• Experience with Airflow and workflow orchestration.
• Hands-on experience with Delta Lake and Unity Catalog.
• Strong CI/CD and DevOps experience.
• Experience with Git and Terraform.
• Strong understanding of cloud-native data and ML architectures.
• Excellent troubleshooting and problem-solving skills.
• Strong communication and collaboration abilities.

Location & Interview Requirement

This is an onsite contract position available in:

• Columbus, Ohio
• Dallas, Texas
• Atlanta, Georgia

Local candidates only.

An onsite interview is required, so candidates should be located within a reasonable distance of one of the listed locations and available to attend the interview in person.

Ideal Candidate

The ideal candidate is a senior MLOps Engineer who can bridge data engineering, machine learning operations, cloud infrastructure, and DevOps.

You should be comfortable working with large-scale Databricks environments, optimizing Spark workloads, developing Python/PySpark pipelines, supporting ML deployments, automating CI/CD processes, managing AWS infrastructure, and maintaining reliable production systems.

Experience building enterprise-grade data and ML platforms with strong governance, automation, monitoring, and scalability will be highly valued.

How to Apply

Interested candidates with 10+ years of experience and strong Databricks/AWS/MLOps expertise should share their updated resume and LinkedIn profile.

📩 Email: yaseen@nortekconsulting.com

Please include your current location, total experience, Databricks/AWS experience, and availability when submitting your profile.

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