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.
Drive sales opportunities and manage the pipeline through RFPs, proposals, and networking activities. Build and maintain strong relationships with current clients to identify new growth opportunities and ensure satisfaction.
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.
