Senior Data/AI engineer

Project description

Build reusable platform capabilities and data products that let quantitative researchers discover, access, and consume data across a complex, disparate landscape, and then accelerate the good prototypes to production.

Responsibilities

• - Design, build, and enhance data pipelines and integration frameworks supporting AI, analytics, and business applications. - Improve data quality, reliability, lineage, and monitoring. - Implement AI first approach: use AI agents as a primary tool to design, build, test, and support solutions - Collaborate closely with business stakeholders, product managers, and engineering teams to translate business objectives into technical solutions.

SKILLS

Must have

• - 10+ years of professional software development, including 5+ years of data engineering experience. - Advanced proficiency in Python, including experience building scalable backend services and data processing solutions. - Strong hands-on experience designing and implementing data pipelines, data integration solutions, and large-scale data processing architectures - Experience working with relational databases and modern data storage technologies, including graph, vector, or analytical data platforms. - Practical experience building and deploying AI/LLM-enabled applications in production environments. - Experience with LangGraph, LangChain, agentic workflows, or orchestration frameworks for AI applications; evaluations. - Familiarity with modern AI tooling and workflows, including and AI-assisted software engineering practices. - Experience operating in agile delivery environments. - Strong communication skills and ability to interact efficiently with various stakeholder groups including business users.

Nice to have

- Experience with Databricks, Spark, Delta Lake, or similar modern data engineering platforms. - Experience designing enterprise data lakes, lakehouses, or large-scale analytical platforms. - Domain experience within Investment Management, Asset Management, Capital Markets, or Financial Services. - Strong understanding of Fixed Income products and related investment workflows. Candidates with significant Fixed Income domain expertise will be considered highly valuable. - Experience with Aurora, Neptune, and other AWS data services. Strong hands-on experience designing and implementing data pipelines, data integration solutions, and large-scale data processing architectures. - Experience architecting, provisioning, and managing production infrastructure on AWS, including services such as ECS Fargate, Lambda, SQS, and related cloud-native technologies. - Experience defining cloud architecture programmatically using Infrastructure-as-Code (IaC) tools such as AWS CDK or Terraform.