Job Description

About Us

We are a rapidly growing organization transforming how people engage with insurance and financial products through a technology-driven, client-first approach. By combining a modern digital platform with an expert advisory network, we empower clients to find the right coverage with transparency, efficiency, and trust.

Our teams are building a deeply data-driven culture where analytics guide product innovation, operational strategy, and customer experience. We work on high-impact initiatives spanning underwriting, marketing, customer engagement, and partner enablement. If you're passionate about designing, building, and scaling production-grade machine learning systems that power smarter decisions and automated experiences, we’d love to meet you.

Who You Are

You are an experienced Machine Learning Engineer who bridges the gap between data science and software engineering. You’re passionate about delivering reliable, scalable, maintainable ML systems that drive measurable business value. You thrive in environments where experimentation, deployment, and continuous improvement are core practices, and you enjoy partnering with data scientists, engineers, and product teams to bring advanced analytics into real-world applications.

Key Responsibilities

• Design, build, and maintain end-to-end machine learning pipelines—from data ingestion to model deployment and monitoring.
• Partner with data scientists to productionize models that are performant, maintainable, and observable.
• Develop robust APIs, batch workloads, and streaming solutions to integrate ML into products, services, and business workflows.
• Implement MLOps practices for automated training, testing, deployment, and monitoring of production models.
• Optimize model performance and scalability, ensuring effective use of compute and storage resources.
• Collaborate with data engineering teams to ensure pipelines are powered by reliable, high-quality data sources.
• Stay current with advances in ML infrastructure, frameworks, and tooling to continuously improve delivery speed and system quality.

Required Qualifications

• 5+ years of experience in machine learning engineering, data science, or software engineering.
• Proficiency in Python and hands-on experience with ML frameworks such as PyTorch, TensorFlow, or Scikit-learn.
• Experience building and deploying models in cloud environments (e.g., AWS, Azure, GCP).
• Strong understanding of data pipelines, feature engineering, and the full model lifecycle.
• Familiarity with containerization and orchestration tools (e.g., Docker, Kubernetes).
• Solid software engineering fundamentals including version control, testing, and CI/CD.
• Strong communication and cross-functional collaboration skills.

Preferred Qualifications

• Experience with MLOps frameworks and tools (e.g., MLflow, Databricks, SageMaker, Vertex AI).
• Familiarity with feature stores and model monitoring platforms.
• Experience with distributed data processing technologies (e.g., Spark, PySpark).
• Background in insurance, financial services, or other data-rich, regulated industries.
• Exposure to experimentation platforms and model governance frameworks.

Security and Privacy Responsibilities

• Adhere to company policies and procedures related to data security and privacy.
• Participate in ongoing training for responsible AI and data handling best practices.
• Treat all client and organizational data with the highest standards of confidentiality.
• Report any security, bias, or privacy incidents promptly.