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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