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
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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
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Development experience using both the AWS and GCP suite of tools.
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Familiarity with SageMaker, Streamlit, web security, credentials and API management tools
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Experience developing repeatable architectural patterns; ability to identify redundancies and eliminate them with these patterns.
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Experience building and deploying webservices in a cloud environment.
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Experience building CICD pipeline using Jenkins or equivalent
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Experience with IAC (Infrastructure as Code) including Cloud Formation, Terraform, or similar
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Expert-level Github experience, including Github Actions
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Strong object oriented development experience using Python, Java, C#
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Familiarity with big data technologies (i.e. Hadoop, Spark, Hive, etc.) and RDBMS platforms such as Redshift, Snowflake or BigQuery
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Experience in end to end model development lifecycle, from ideation through post production monitoring.
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Experience with workflow automation platforms (Apache Airflow, Autosys, similar)
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Experience with Solution Design and Architecture of data pipelines
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Basic understanding of Data Science model development life cycle
Preferred Skills
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Fundamentally strong with Data Structures and algorithms.
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Experience working with Docker, Kubernetes and EC2 environment.
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Experience building ML and data pipeline and orchestration services
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Basic understanding of ML frameworks i.e. Tensorflow, Anacoda, Scikit Learn,
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Experience working in an Agile framework.
Qualifications
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ML engineering, data manipulation and application development
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Python development experience
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Working with IAC, developing CICD pipelines
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Experience in the insurance or broader financial services industry
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SQL development experience
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Familiarity with emerging data centric technologies such generative AI, Agentic workflows, and embedding LLM’s into automated processes
