Information Technology_USA – USA_Developer

**Please strictly adpersonre to tperson following resume naming convention:
ALL CAPS, NO SPACES B/T UNDERSCORESBill Rate market rate – market ratePTN_US_GBAMSREQID_CandidateBeelineID
i.e. PTN_US_9999999_SKIPJOHNSON0413MSP Owner: Michelle Lee
Location: Sunnyvale, CA
Duration: 6 months
GBaMS ReqID: 103225566-8+ years experience required in tperson following skills:
Digital : Microsoft Azure
Digital : Machine Learning
Digital : DevOps
Digital : DockerJob Summary: We are seeking a skilled and proactive Mops Engineer to join our team and personlp operationalize machine learning models at scale. Tperson ideal candidate will have experience in deploying, monitoring, and maintaining ML models in production environments, and will work closely with data scientists, software engineers, and DevOps teams to ensure seamless integration and performance.Key Responsibilities: Design, build, and maintain scalable ML infrastructure and pipelines.
Automate model deployment, versioning, and rollback processes.
Monitor model performance and data drift in production.
Collaborate with data scientists to transition models from experimentation to production.
Implement CI/CD workflows for ML systems.
Ensure compliance with data governance and security standards.
Optimize resource usage and cost-efficiency of ML workloads.
Maintain documentation and best practices for Mops processes.

Required Qualifications: Bachelor’s or Master’s degree in Computer Science, Engineering, or related field.
3+ years of experience in Mops, DevOps, or ML Engineering.
Proficiency in Python and ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).
Experience with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes).
Familiarity with ML lifecycle tools (MLflow, Kubeflow, SageMaker, etc.).
Strong understanding of CI/CD, Git, and automation tools.
Excellent problem-solving and communication skills.

Preferred Qualifications: Experience with data versioning tools (e.g., DVC).
Knowledge of feature stores and model registries.
Exposure to real-time inference and streaming data systems.

SysMind