C2C role: ML-Ops Engineer || San Ramon, CA Need local profiles/ No relocation

[Yogesh Singh]

Hi Bench sales,

 

Please share the resumes of H1/H4 for the role, mentioning visa status, current location and LI ID of the candidate.

 

Notes from the hiring manager:
This team has all Ex-Meta, Ex-Google, Ex-Netflix type of Managers… They require strong and hands-on experience in MLOps, Python, etc. Knowledge of AIML is good, but it’s not the primary skill.

 

C2C role:

ML Ops Engineer 10+ years

San Ramon, CA, onsite (Need locals only)

Rate:65/hr. C2C

Predictive AI team seeking a ML Ops Engineer to drive the full lifecycle of machine learning solutions.

 Key Responsibilities

                •             Develop and maintain ML pipelines using tools like MLflow, Kubeflow, or Vertex AI.

                •             Automate model training, testing, deployment, and monitoring in cloud environments (e.g., GCP, AWS, Azure).

                •             Implement CI/CD workflows for model lifecycle management, including versioning, monitoring, and retraining.

                •             Monitor model performance using observability tools and ensure compliance with model governance frameworks (MRM, documentation, explainability).

                •             Collaborate with engineering teams to provision containerized environments and support model scoring via low-latency APIs.

                •             Leverage AutoML tools (e.g., Vertex AI AutoML, H2O Driverless AI) for low-code/no-code model development, documentation automation, and rapid deployment.

 Qualifications

                •             10+ years of professional experience in Software Engineering & 3+ years in AIML, Machine Learning Model Operations.

                •             Strong proficiency in Java and Python, SQL, and ML libraries (e.g., scikit-learn, XGBoost, TensorFlow, PyTorch).

                •             Experience with cloud platforms and containerization (Docker, Kubernetes).

                •             Familiarity with data engineering tools (e.g., Airflow, Spark) and ML Ops frameworks.

                •             Solid understanding of software engineering principles and DevOps practices.

                •             Ability to communicate complex technical concepts to non-technical stakeholders.

 Thanks

Yogesh Pratap Singh

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