Description :

What youll do :

- Architect and evolve a scalable, secure ML platform that supports the end to end ML lifecycle, including data preparation, training, evaluation, deployment, and monitoring.

- Design and implement core ML infrastructure for model training, hyperparameter tuning, experiment tracking, and model registry, using cloud native and open source technologies.

- Orchestrate ML workflows using tools such as Kubeflow, SageMaker, MLflow, Vertex AI, or Databricks, ensuring reproducibility and robust automation.

- Partner with Data Engineers to build reliable, high quality data pipelines and feature pipelines that provide model-ready datasets at scale.

- Implement and improve CI/CD for ML, including automated testing, validation, and safe rollout/rollback of models and data pipelines.

- Optimize ML workload performance and cost across compute, storage, and networking layers on public cloud (AWS, GCP, or Azure).

- Embed observability and governance into the platform, including logging, tracing, model performance monitoring, and drift detection.

- Collaborate with security, compliance, and data governance teams to ensure the platform adheres to Guidewires standards for security, privacy, and auditability.

- Provide technical leadership and mentorship to other engineers, influencing architectural decisions, coding standards, and best practices for ML platform and MLOps.

- Continuously explore and apply AI/automation (including GenAI) to improve developer and data scientist productivity, platform reliability, and operational efficiency.

What youll bring :

Required :

- Demonstrated ability to embrace AI and use data-driven insights to drive innovation, productivity, and continuous improvement in your current role.

- Bachelors or Masters degree in Computer Science, Engineering, or a related field.

- 10+ years of software engineering experience, including 5+ years working on ML platforms or infrastructure.

- Expertise in building large-scale distributed systems and microservices, with solid understanding of system design and architecture.

- Strong programming skills in Python, Go, or Java, with emphasis on writing clean, testable, maintainable code.

- Hands-on experience with containerization and orchestration, for example Docker and Kubernetes.

- Familiarity with MLOps tools such as MLflow, Kubeflow, SageMaker and how they fit into an end-to-end ML platform.

- Cloud platform experience on AWS, GCP, or Azure, including core services for compute, storage, networking, and identity.

- Experience with statistical learning algorithms (e.g., GLM, XGBoost, Random Forest) and deep learning approaches (e.g., neural networks, transformers), with a practical understanding of how they are trained and deployed.

- Strong communication, leadership, and problem-solving skills, with the ability to influence across functions and work effectively in a global, distributed environment.

Preferred :

- Experience with real-time model inference and streaming ML pipelines, including low-latency serving and online feature computation.

- Deep knowledge of model governance, reproducibility, and monitoring, including experiment lineage, versioning, and approval workflows.

- Understanding of model performance metrics and drift detection, and experience implementing monitoring for data drift, concept drift, and model quality.

- Exposure to feature stores (e.g., Feast, Tecton) and workflow orchestration tools (e.g., Airflow, Argo) in production environments.

- Familiarity with regulatory and compliance considerations for ML systems, including model auditability, interpretability, and data privacy laws such as CCPA/GDPR.

- Experience with real-time data pipelines and streaming technologies such as Kafka, Flink, or Spark Structured Streaming.

- Experience using TeamCity and Terraform (or similar tools) for infrastructure-as-code and CI/CD of platform components.

- Domain experience in insurance or related industries (such as banking or finance), or a demonstrated ability to ramp quickly in highly regulated domains