Pay: Salary not listed in the saved posting
Work setup: Unconfirmed
Employment: Unconfirmed

What you’ll work on

Research, experiment with, and implement suitable Generative and ML algorithms, tools and technologies.
Work with junior engineers and peers to provide mentorship and thought leadership.
Collaborate with partners Enterprise Data, Data Science, Business, Cloud Enablement Team, and Enterprise Architecture teams

From the employer’s posting

Responsibilities Research, experiment with, and implement suitable Generative and ML algorithms, tools and technologies. Participate in identifying and assessing opportunities i.e. value of new data sources and analytical techniques and technology, to ensure ongoing competitive advantage.

Accountable for design, development and maintenance of Models as Service Work with junior engineers and peers to provide mentorship and thought leadership. Be comfortable presenting new concepts to technical audiences. Collaborate with partners Enterprise Data, Data Science, Business, Cloud Enablement Team, and Enterprise Architecture teams

Work with junior engineers and peers to provide mentorship and thought leadership. Be comfortable presenting new concepts to technical audiences. Collaborate with partners Enterprise Data, Data Science, Business, Cloud Enablement Team, and Enterprise Architecture teams Delivery of critical milestones for model deployment in the AWS and GCP clouds.

What you’ll bring

All qualifications

Core experience

Familiarity with SageMaker, Streamlit, web security, credentials and API management tools
Python development experience
Experience developing repeatable architectural patterns; ability to identify redundancies and eliminate them with these patterns.
Experience in the insurance or broader financial services industry
Experience building and deploying webservices in a cloud environment.
SQL development experience

Preferred experience

Experience working with Docker, Kubernetes and EC2 environment.
Experience building ML and data pipeline and orchestration services
Experience working in an Agile framework.

Qualification wording

Familiarity with SageMaker, Streamlit, web security, credentials and API management tools

Python development experience

Experience developing repeatable architectural patterns; ability to identify redundancies and eliminate them with these patterns.

Experience in the insurance or broader financial services industry

Experience building and deploying webservices in a cloud environment.

SQL development experience

Experience working with Docker, Kubernetes and EC2 environment.

Experience building ML and data pipeline and orchestration services

Experience working in an Agile framework.

Tools in this posting

Java
Python
AWS
Docker
Google Cloud (GCP)
Hadoop
Hive
Kubernetes
Redshift
SageMaker
Snowflake
Spark
Terraform
Airflow
scikit-learn
Streamlit
TensorFlow
SQL
C#
BigQuery

Source

- Expert-level Github experience, including Github Actions - Strong object oriented development experience using Python, Java, C# - Familiarity with big data technologies (i.e. Hadoop, Spark, Hive, etc.) and RDBMS platforms such as Redshift, Snowflake or BigQuery

- ML engineering, data manipulation and application development - Python development experience - Working with IAC, developing CICD pipelines

- Collaborate with partners Enterprise Data, Data Science, Business, Cloud Enablement Team, and Enterprise Architecture teams - Delivery of critical milestones for model deployment in the AWS and GCP clouds. - Adopt and promote MLOps best practices to the Data Science community.

Minimum Requirements - Development experience using both the AWS and GCP suite of tools. - Familiarity with SageMaker, Streamlit, web security, credentials and API management tools

- Fundamentally strong with Data Structures and algorithms. - Experience working with Docker, Kubernetes and EC2 environment. - Experience building ML and data pipeline and orchestration services

- Strong object oriented development experience using Python, Java, C# - Familiarity with big data technologies (i.e. Hadoop, Spark, Hive, etc.) and RDBMS platforms such as Redshift, Snowflake or BigQuery - Experience in end to end model development lifecycle, from ideation through post production monitoring.

- Development experience using both the AWS and GCP suite of tools. - Familiarity with SageMaker, Streamlit, web security, credentials and API management tools - Experience developing repeatable architectural patterns; ability to identify redundancies and eliminate them with these patterns.

- Experience building CICD pipeline using Jenkins or equivalent - Experience with IAC (Infrastructure as Code) including Cloud Formation, Terraform, or similar - Expert-level Github experience, including Github Actions

- Experience in end to end model development lifecycle, from ideation through post production monitoring. - Experience with workflow automation platforms (Apache Airflow, Autosys, similar) - Experience with Solution Design and Architecture of data pipelines

- Experience building ML and data pipeline and orchestration services - Basic understanding of ML frameworks i.e. Tensorflow, Anacoda, Scikit Learn, - Experience working in an Agile framework.

- Experience in the insurance or broader financial services industry - SQL development experience - Familiarity with emerging data centric technologies such generative AI, Agentic workflows, and embedding LLM’s into automated processes

Job description

View original posting ↗

IND Staff Engineer - GCC094

We’re determined to make a difference and are proud to be an insurance company that goes well beyond coverages and policies. Working here means having every opportunity to achieve your goals – and to help others accomplish theirs, too. Join our team as we help shape the future.

Responsibilities

Research, experiment with, and implement suitable Generative and ML algorithms, tools and technologies.
Participate in identifying and assessing opportunities i.e. value of new data sources and analytical techniques and technology, to ensure ongoing competitive advantage.
Review work with leadership and partners on an ongoing basis to calibrate deliverables against expectations.
Accountable for design, development and maintenance of Models as Service
Work with junior engineers and peers to provide mentorship and thought leadership. Be comfortable presenting new concepts to technical audiences.
Collaborate with partners Enterprise Data, Data Science, Business, Cloud Enablement Team, and Enterprise Architecture teams
Delivery of critical milestones for model deployment in the AWS and GCP clouds.
Adopt and promote MLOps best practices to the Data Science community.

Minimum Requirements

Development experience using both the AWS and GCP suite of tools.
Familiarity with SageMaker, Streamlit, web security, credentials and API management tools
Experience developing repeatable architectural patterns; ability to identify redundancies and eliminate them with these patterns.
Experience building and deploying webservices in a cloud environment.
Experience building CICD pipeline using Jenkins or equivalent
Experience with IAC (Infrastructure as Code) including Cloud Formation, Terraform, or similar
Expert-level Github experience, including Github Actions
Strong object oriented development experience using Python, Java, C#
Familiarity with big data technologies (i.e. Hadoop, Spark, Hive, etc.) and RDBMS platforms such as Redshift, Snowflake or BigQuery
Experience in end to end model development lifecycle, from ideation through post production monitoring.
Experience with workflow automation platforms (Apache Airflow, Autosys, similar)
Experience with Solution Design and Architecture of data pipelines
Basic understanding of Data Science model development life cycle

Preferred Skills

Fundamentally strong with Data Structures and algorithms.
Experience working with Docker, Kubernetes and EC2 environment.
Experience building ML and data pipeline and orchestration services
Basic understanding of ML frameworks i.e. Tensorflow, Anacoda, Scikit Learn,
Experience working in an Agile framework.

Qualifications

ML engineering, data manipulation and application development
Python development experience
Working with IAC, developing CICD pipelines
Experience in the insurance or broader financial services industry
SQL development experience
Familiarity with emerging data centric technologies such generative AI, Agentic workflows, and embedding LLM’s into automated processes

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