Offer summary

Qualifications:

8-14 years of experience in ML Ops and Enterprise Data Operations (EDO)., Hands-on experience with AWS services, particularly SageMaker, Lambda, and S3., Strong programming skills in Python and SQL, with knowledge of classification models., Familiarity with data governance and metadata management tools..

Key responsabilities:

Deploy models using SageMaker pipelines and jobs or step functions/EMR.

Design and maintain scalable AWS SageMaker Pipelines for ML model workflows.

Automate ML workflows with tools like MLflow and Airflow.

Ensure robust CI/CD pipelines for ML workflows and maintain data quality standards.

Job description

Job Title : ML Ops Engineer -(AWS Sagemaker):
Location : Woodland Hills, CA | 8 - 14 years of experience (Remote Role)
Full Time Role

Job Description
ML Ops Engineer

The Position is for a results-driven ML Ops Data Engineer with a solid foundation in Enterprise Data Operations (EDO) and hands-on experience in AWS SageMaker Pipelines, MLflow, and other AWS services. The Position will play a key role in the following assignments:
1. Deploying models built by data scientist as batch models using SageMaker pipelines & jobs OR step
functions/EMR.
2. Implementing ML Flow server for the Enterprise

Other Responsibilities:
• Design, implement, and maintain scalable AWS SageMaker Pipelines for training, validation, deployment,
and monitoring of machine learning models.
• Automate and operationalize ML workflows using tools like MLflow, Airflow, and AWS Lambda.
• Set up and manage MLflow tracking servers for experiment tracking and model registry.
• Build and optimize classification models using large-scale datasets stored in Amazon S3 and integrated
with AWS ML services.
• Ensure robust CI/CD pipelines for ML workflows using tools such as CodePipeline/ GitHub Actions/ Azure DevOps.
• Maintain enterprise data quality, lineage, and governance standards in alignment with EDO frameworks.
• Integrate ML pipelines into broader enterprise data architecture, including data lakes, warehouses, and
business systems.

Required Skills:
• Hands-on experience with AWS services, especially SageMaker Pipelines, Lambda, and S3.
• Proficient in setting up and managing MLflow servers for model lifecycle tracking.
• Strong Python and SQL programming skills.
• Solid understanding of classification models and supervised learning techniques.
• Experience implementing data pipelines using cloud-native and containerized services (e.g., Docker, Kubernetes).
• Familiarity with data governance, lineage, and metadata management (e.g., Collibra, Informatica, Alation)
• Strong knowledge of Enterprise Data Operations (EDO) practices.

Good to Have Skills:
• Experience in Insurance Domain
• Experience deploying real time models on SageMaker endpoints.
• Experience with AWS services such as IAM, SNS, Cloudwatch
• Experience with snowflake databases and relational data sets.

Diverse Lynx LLC is an Equal Employment Opportunity employer. All qualified applicants will receive due consideration for employment without any discrimination. All applicants will be evaluated solely on the basis of their ability, competence and their proven capability to perform the functions outlined in the corresponding role. We promote and support a diverse workforce across all levels in the company.

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