Orchestrate production pipelines on Databricks and manage model lifecycles using MLflow and Unity Catalog. Ensure operational continuity by managing compute resources, cost-monitoring tags, and coordinating with development squads.
Design and optimize enterprise data pipelines and lakehouse storage layers using Databricks to support clinical application backends. Collaborate with cross-functional teams to build API-driven endpoints and ensure data compliance with regulatory standards like GxP and HIPAA.
You will co-develop batch and streaming data pipelines and semantic models using Databricks while defining data quality rules and SLOs. Additionally, you will mentor domain teams on data product ownership and engineering best practices to ensure scalable and auditable AI solutions.
Design and engineer robust machine learning features on Databricks to solve complex business challenges. Ensure strict consistency between training and production environments while maintaining scalable data governance standards.
The engineer will evaluate, deploy, and operate low-code tools and agent builders within the organization's cloud infrastructure. They are responsible for integrating these tools with corporate systems and writing custom Python or TypeScript code to handle complex requirements.
Design, develop, and maintain robust backend services using Go within a microservices architecture. Collaborate with an AI-driven team to build scalable data products and contribute to technical architectural standards.
Design, develop, and maintain robust backend services using Java within a microservices architecture. Collaborate with an AI-driven team to build scalable solutions and contribute to architectural decision-making.
The Senior Data Scientist will take ownership of the existing credit score model, retraining it and incorporating new features to improve performance. They will also evaluate risk levels, calculate dynamic credit lines, and ensure the model is properly packaged for production.
Design and implement transactional and credit behavior features for risk models while ensuring data governance and metadata standards. Manage the deployment pipeline and ensure consistency between training and production environments.
Lead end-to-end data migration initiatives and define data architecture decisions across cloud platforms. Act as the primary liaison between engineering, product, and business stakeholders to ensure project alignment and successful delivery.
You will build and deploy LLM-based agents on top of platform scaffolding while integrating them into banking systems. Additionally, you will define evaluation metrics, monitor performance, and document processes to ensure teams can operate agents independently.
You will be responsible for industrializing, deploying, and scaling machine learning models into production environments while ensuring MLOps best practices. You will also design end-to-end training and inference pipelines while collaborating with data scientists and engineers to align technical solutions with business needs.
Design and build backend services for analytics and reporting while ensuring high standards of code quality and performance. Collaborate with cross-functional teams to deliver features and mentor engineers to improve development practices.
You will be responsible for industrializing, deploying, and scaling machine learning models into production environments while ensuring reliability and traceability. The role involves building CI/CD pipelines, managing model registries, and collaborating with cross-functional teams to align technical solutions with business needs.
The role involves co-developing batch and streaming pipelines on Databricks while defining data contracts and quality rules. Additionally, the engineer will mentor domain teams on data modeling and best engineering practices.
The role involves co-developing batch and streaming pipelines on Databricks while defining data contracts and quality rules. You will also mentor domain teams on data modeling and engineering best practices to improve platform templates.
Design and build agentic systems using frontier LLMs and develop document understanding pipelines for complex legal contracts. Collaborate with legal and commercial stakeholders to translate requirements into system logic and build evaluation sets to measure accuracy.
The Data Engineer Lead will co-develop batch and streaming pipelines while defining data contracts and quality rules. They are also responsible for mentoring domain engineers and guiding teams on data modeling and product ownership.
The role involves co-developing batch and streaming data pipelines on Databricks while defining data contracts and quality rules. Additionally, the engineer will mentor domain teams on data modeling and best practices to improve platform templates and tooling.
