Machine Learning

Trigent Software Private Limited

Job Location:

Delhi - India

Monthly Salary: Not Disclosed

Posted on: 6 hours ago

Vacancies: 1 Vacancy

Job Summary

Key Responsibilities

Understand the complex requirements and needs of business stakeholders developing strong relationships and how machine learning solutions can meet those needs in order to support the achievement of business strategy
Work collaboratively with colleagues to productionise machine learning models including pipeline design and development testing and deployment ensuring that the original intent and knowledge is carried over to production
Create frameworks to ensure robust monitoring of machine learning models within production environment ensuring models are delivering expected quality and performance understanding and addressing any shortfalls for example through retraining
Work with and lead both direct reports and wider teams in an agile way within multi-disciplinary data and analytics teams to achieve agreed project and scrum outcomes

Machine Learning Engineer with experience in designing building and deploying end-to-end ML workflows on AWS utilising SageMaker Pipelines and SageMaker Endpoints.
Deep understanding of core AWS services including S3 KMS Lambda Secrets Manager and CI/CD tools such as CodeBuild and CodePipeline to automate deployments.
Skilled in orchestrating complex workflows with Airflow and integrating streaming data from Kafka.

Short Description of the Job: Drive and embed the deployment automation maintenance and monitoring of machine learning models and algorithms to ensure that that they work effectively in a production environment.
Key Responsibilities

Understand the complex requirements and needs of business stakeholders developing strong relationships and how machine learning solutions can meet those needs in order to support the achievement of business strategy
Work collaboratively with colleagues to productionise machine learning models including pipeline design and development testing and deployment ensuring that the original intent and knowledge is carried over to production
Create frameworks to ensure robust monitoring of machine learning models within production environment ensuring models are delivering expected quality and performance understanding and addressing any shortfalls for example through retraining
Work with and lead both direct reports and wider teams in an agile way within multi-disciplinary data and analytics teams to achieve agreed project and scrum outcomes

Key Responsibilities Understand the complex requirements and needs of business stakeholders developing strong relationships and how machine learning solutions can meet those needs in order to support the achievement of business strategy Work collaboratively with colleagues to productionise machin...

Key Responsibilities

Understand the complex requirements and needs of business stakeholders developing strong relationships and how machine learning solutions can meet those needs in order to support the achievement of business strategy
Work collaboratively with colleagues to productionise machine learning models including pipeline design and development testing and deployment ensuring that the original intent and knowledge is carried over to production
Create frameworks to ensure robust monitoring of machine learning models within production environment ensuring models are delivering expected quality and performance understanding and addressing any shortfalls for example through retraining
Work with and lead both direct reports and wider teams in an agile way within multi-disciplinary data and analytics teams to achieve agreed project and scrum outcomes

Machine Learning Engineer with experience in designing building and deploying end-to-end ML workflows on AWS utilising SageMaker Pipelines and SageMaker Endpoints.
Deep understanding of core AWS services including S3 KMS Lambda Secrets Manager and CI/CD tools such as CodeBuild and CodePipeline to automate deployments.
Skilled in orchestrating complex workflows with Airflow and integrating streaming data from Kafka.

Understand the complex requirements and needs of business stakeholders developing strong relationships and how machine learning solutions can meet those needs in order to support the achievement of business strategy
Work collaboratively with colleagues to productionise machine learning models including pipeline design and development testing and deployment ensuring that the original intent and knowledge is carried over to production
Create frameworks to ensure robust monitoring of machine learning models within production environment ensuring models are delivering expected quality and performance understanding and addressing any shortfalls for example through retraining
Work with and lead both direct reports and wider teams in an agile way within multi-disciplinary data and analytics teams to achieve agreed project and scrum outcomes