Technical Lead
Apply NowCompany: Tata Consultancy Services
Location: Woodland Hills, CA 91367
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.
Salary Range- $130,000-$140,000 a year
#LI-OJ1
#LI-DR1
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.
Salary Range- $130,000-$140,000 a year
#LI-OJ1
#LI-DR1