> Markdown version of [/jobs/ext/4577328-git-mlops-engineer](https://www.wearedevelopers.com/jobs/ext/4577328-git-mlops-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Git MLOps Engineer

- **Company:** Artech
- **Location:** Tampa, FL, United States
- **Experience:** Expert
- **Salary:** $83,200.0 - $93,600.0
- **Contract:** Temporary contract
- **Skills:** Amazon Web Services, Microsoft Azure, Cloud Computing, Computer Programming, Continuous Integration, Information Engineering, Extract Transform Load (ETL), DevOps, Github, Python (Programming Language), Machine Learning, Tensorflow, Prometheus, Azure Machine Learning, Software Engineering, Data Streaming, YAML, Data Logging, Pytorch, Grafana, Google Vertex AI, Multi-Cloud, Git, Containerization, Scikit Learn, Kubernetes, Information Technology, Machine Learning Operations, Virtual Agents, Terraform, Software Version Control, Docker, Jenkins, Databricks
- **Published:** November 5, 2026
- **Apply:** https://www.thejobnetwork.com/job/553f9ac7-800f-4abb-bd81-2bd40257c9fd/git-mlops-engineer

## About the Role

" Master's Degree in Computer Science, Data Engineering, Software Engineering preferred
" -At least 4 years of experience in software engineering or data engineering
" Experience operating debt at scale on Databricks
" Prior work with healthcare analytics packages
" Designing CI/CD pipelines for Databricks Asset Bundles
" Provisioning Azure Databricks workspaces and managing isolation patterns
" Building Python CLI tools with argparse and YAML configuration
" Designing AI agent systems for development workflows
" Familiarity with corporate network constraints
" Experience with multi-cloud data flows, ML workloads.Troubleshoot production issues and perform root cause analysis.Maintain documentation| standards| and best practices for MLOps processes.Required SkillsStrong programming skills in Python.Experience with ML frameworks such as TensorFlow| PyTorch| or Scikit-learn.Hands-on experience with CI/CD tools (Azure DevOps| Jenkins| GitHub Actions).Knowledge of Docker| Kubernetes| and Infrastructure as Code (Terraform| ARM| etc.).Experience with cloud platforms (Azure ML| AWS SageMaker| GCP Vertex AI).Familiarity with version control systems such as Git.Knowledge of monitoring and logging tools (Prometheus| Grafana| ELK| Azure Monitor).Understanding of data engineering concepts and ETL pipelines.Preferred QualificationsBachelors or Masters degree in Computer Science| Data Science| Engineering| or related field.Relevant certifications in Azure| AWS| Kubernetes| or DevOps.Experience implementing enterprise-scale AI/ML solutions.Key CompetenciesProblem-solving and analytical
thinkingAutomation-first mindsetCollaboration and stakeholder managementContinuous learning and innovationStrong communication skills
Essential Skills: Role Summary:The MLOps Engineer is responsible for building| automating| deploying| monitoring| and maintaining Machine Learning (ML) models and platforms in production environments. The role bridges Data Science| Software Engineering| and DevOps practices to ensure scalable| reliable| and secure ML solutions.Key ResponsibilitiesDesign| build| and maintain end-to-end ML pipelines for training| testing| deployment| and monitoring.Automate CI/CD processes for machine learning models and data workflows.Deploy and manage ML models on cloud platforms such as Azure| AWS| or GCP.Implement model monitoring| performance tracking| drift detection| and retraining strategies.Collaborate with Data Scientists| Data Engineers| and Application Development teams to operationalize ML solutions.Manage containerization and orchestration using Docker and Kubernetes.Ensure security| compliance| scalability| and reliability of ML platforms.Optimize infrastructure utilization and
cloud costs for ML workloads.Troubleshoot production issues and perform root cause analysis.Maintain documentation| standards| and best practices for MLOps processes.Required SkillsStrong programming skills in Python.Experience with ML frameworks such as TensorFlow| PyTorch| or Scikit-learn.Hands-on experience with CI/CD tools (Azure DevOps| Jenkins| GitHub Actions).Knowledge of Docker| Kubernetes| and Infrastructure as Code (Terraform| ARM| etc.).Experience with cloud platforms (Azure ML| AWS SageMaker| GCP Vertex AI).Familiarity with version control systems such as Git.Knowledge of monitoring and logging tools (Prometheus| Grafana| ELK| Azure Monitor).Understanding of data engineering concepts and ETL pipelines.Preferred QualificationsBachelors or Masters degree in Computer Science| Data Science| Engineering| or related field.

## Description

" Design and implement advanced analytics solutions for healthcare payers
" Analyze intricate challenges and develop actionable insights
" Mentor and guide junior team members in their professional growth
" Maintain exceptional standards in deliverables
" Build and nurture substantial relationships with clients
" Navigate complex project environments and adapt to changing needs
" Collaborate with healthcare experts and data scientists to drive innovation
" Contribute to the development of impactful solutions in a consulting setting, Role Descriptions: Role Summary:The MLOps Engineer is responsible for building| automating| deploying| monitoring| and maintaining Machine Learning (ML) models and platforms in production environments. The role bridges Data Science| Software Engineering| and DevOps practices to ensure scalable| reliable| and secure ML solutions.Key ResponsibilitiesDesign| build| and maintain end-to-end ML pipelines for training| testing| deployment| and monitoring.Automate CI/CD processes for machine learning models and data workflows.Deploy and manage ML models on cloud platforms such as Azure| AWS| or GCP.Implement model monitoring| performance tracking| drift detection| and retraining strategies.Collaborate with Data Scientists| Data Engineers| and Application Development teams to operationalize ML solutions.Manage containerization and orchestration using Docker and Kubernetes.Ensure security| compliance| scalability| and reliability of ML platforms.Optimize infrastructure utilization and cloud costs for