Git MLOps Engineer

Job Title: Git MLOps Engineer
Location: Tampa, FL - ONSITE
Duration: 6 months (Extendable)
Pay Range: $40-45/Hour on W2/C2C (All Inclusive)
Request ID: 114730-1


Position Summary:
Git & ML-Ops Engineer, Senior Associate-

Responsibilities:
" 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

Tech/Background:
" 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



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 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.