MLOPS Engineer in c2c requirements Plano, TX

Role: MLOPS Engineer

Location: Reston, VA/Plano, TX(Day 1 Onsite).

Position via Hexaware

Job Description:-

MLOPS (Machine Learning Operations): The MLOPS Engineer will design and build scalable machine learning infrastructure, ensuring smooth deployment, monitoring, and lifecycle management of ML models.
Responsibilities include automating workflows, enabling continuous integration/continuous deployment (CI/CD) pipelines.
Develop and Maintain ML Infrastructure: Build and maintain ML pipelines that support model training, testing, deployment, and monitoring.
Model Deployment: Implement efficient processes for deploying ML models in production environments, such as cloud platforms or on-premises infrastructure.
Set up CI/CD pipelines for continuous integration and delivery of ML models. Automation and Scaling: Automate model retraining, validation, and performance monitoring processes.
Collaboration with Data Scientists: Work closely with data scientists to streamline the model development lifecycle and ensure models can easily be transitioned to production.
Monitoring and Optimization: Monitor ML models in production for accuracy and performance and troubleshoot any deployment or scaling issues.
Infrastructure as Code (IaC): Develop infrastructure as code to manage cloud resources for ML workloads.
Versioning and Experimentation Tracking: Implement model versioning, experiment tracking, and reproducibility techniques.
Security and Compliance: Ensure models comply with organizational security standards and regulatory guidelines.
Containerization: Hands-on experience with Docker, Kubernetes, or other container orchestration systems.
CI/CD Tools: Knowledge of Jenkins, GitLab, CircleCI, or other CI/CD tools for automation.
Data Pipelines: Experience orchestration tools for managing data pipelines.
Version Control: Familiarity with Git for code versioning.
DevOps Experience: Basic understanding of DevOps tools and practices (e.g., Terraform)
8+ years of experience on Python and AWS Services using ML Models Implementation to Production