At a glanceSummarised by Seekless from the posting.
Must have19
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
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
Nice to have5
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
Skills
AWS
GCP
SageMaker
Streamlit
Jenkins
Cloud Formation
Terraform
Github
Github Actions
Python
Java
C#
Hadoop
Spark
Hive
Redshift
Snowflake
BigQuery
Apache Airflow
Autosys
Docker
Kubernetes
EC2
Tensorflow
Scikit Learn
SQL
Generative AI
IND Staff Engineer - GCC094
About the company
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
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Research, experiment with, and implement suitable Generative and ML algorithms, tools and technologies.
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Participate in identifying and assessing opportunities i.e. value of new data sources and analytical techniques and technology, to ensure ongoing competitive advantage.
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Review work with leadership and partners on an ongoing basis to calibrate deliverables against expectations.
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Accountable for design, development and maintenance of Models as Service
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Work with junior engineers and peers to provide mentorship and thought leadership. Be comfortable presenting new concepts to technical audiences.
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Collaborate with partners Enterprise Data, Data Science, Business, Cloud Enablement Team, and Enterprise Architecture teams
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Delivery of critical milestones for model deployment in the AWS and GCP clouds.
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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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