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 Sage Maker 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