Offer summary

Qualifications:

Solid foundation in Enterprise Data Operations (EDO) and hands-on experience with AWS Sage Maker Pipelines., Proficient in Python and SQL programming, with strong knowledge of classification models and supervised learning techniques., Experience with cloud-native and containerized services, such as Docker and Kubernetes, is essential., Familiarity with data governance and metadata management tools like Collibra, Informatica, and Alation..

Key responsabilities:

Deploy models built by data scientists using Sage Maker pipelines and jobs or step functions/EMR.

Design, implement, and maintain scalable AWS Sage Maker Pipelines for machine learning model workflows.

Automate ML workflows using tools like ML flow, Airflow, and AWS Lambda, ensuring robust CI/CD pipelines.

Integrate ML pipelines into the broader enterprise data architecture, maintaining data quality and governance standards.

Job description

Job Title: ML Ops Engineer

Location: Woodland Hills, CA (REMOTE)

Position : FULL TIME

Skills: AWS | Docker , Sage Maker , EDO(Enterprise Data Operations) , data governance (e.g., Collibra, Informatica, Alation)

Job Description:

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 Sage Maker Pipelines, ML flow, 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 Sage Maker pipelines & jobs OR step

functions/EMR.

2. Implementing ML Flow server for the Enterprise

Other Responsibilities:

• Design, implement, and maintain scalable AWS Sage Maker Pipelines for training, validation, deployment,

and monitoring of machine learning models.

• Automate and operationalize ML workflows using tools like ML flow, Airflow, and AWS Lambda.

• Set up and manage ML flow 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 Code Pipeline/ 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 Sage Maker Pipelines, Lambda, and S3.

• Proficient in setting up and managing ML flow 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 Sage Maker endpoints.

• Experience with AWS services such as IAM, SNS, Cloud watch

• 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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