Software Engineer – AI

Responsibilities You will be part of the team building the platforms that power sustainability and climate investment decisions for some of the world's largest asset managers and asset owners. The work is hands-on and high-impact: designing and shipping cloud-native data pipelines, modernizing legacy systems into distributed, resilient architectures capable of processing ESG and climate datasets at scale. You will own meaningful pieces of the stack — from pipeline architecture and deployment to observability and reliability — working closely with Product, Data Operations, QA, and Infrastructure to deliver systems that meet real client SLAs in a regulated environment. AI-driven workflows are increasingly central to how the team operates, and you will build toward that future directly, not observe it from the sidelines. This is a team that is actively raising its engineering bar — investing in better deployment pipelines, stronger platform architecture, and smarter developer tooling. For an early-career engineer, that means learning by doing, growing fast, and seeing your work reach clients at global scale. Your Key Responsibilities Design, build, and own AI/ML pipelines that automate sustainability data ingestion, validation, and quality assurance workflows — processing millions of ownership and revenue data points across 64,000+ issuers globally. Develop and deploy cloud-native services on AWS, Azure, or GCP using Python, translating complex financial screening methodologies into scalable, production-grade systems. Architect and maintain distributed data pipelines that ingest structured and unstructured data from global financial data providers, applying automated rules to compute screening flags at scale. Build and tune AI models that reduce manual analyst effort, improve data consistency, and enable coverage of issuer universes that are not reachable through human-only workflows. Implement robust QA automation, validation logic, and override mechanisms to ensure data integrity in a client-facing, regulated environment where accuracy directly influences investment decisions. Collaborate with Data Scientists, Product Managers, and Data Operations to translate methodology requirements into reliable engineering solutions. Contribute to platform observability, deployment pipelines, and engineering standards that keep production systems stable, performant, and secure at scale. Continuously improve your understanding of AI-driven data engineering — growing from owning discrete pipeline components toward end-to-end system design as the platform scales. Your skills and experience that will help you excel 2–6 years of hands-on software engineering experience, with a track record of building and shipping production systems — not just prototypes. Strong Python proficiency — you write clean, maintainable, production-grade code and are comfortable owning it end-to-end. Applied AI/ML experience — you have built, trained, or deployed machine learning models in a real-world context. Familiarity with frameworks such as PyTorch, TensorFlow, HuggingFace, or LangChain is a strong plus. Cloud engineering fluency — hands-on experience designing and deploying services on AWS, Azure, or GCP, including managed data and compute services. Data pipeline and distributed systems thinking — you understand how large-scale data flows through a system, and can design pipelines that are reliable, observable, and built to scale. QA and validation mindset — you care about data correctness and build automated checks, not just features. Experience with QA automation frameworks is an advantage. Comfort with ambiguity — you can take a loosely defined problem, ask the right questions, and drive toward a well-engineered solution without waiting to be told every step. Curiosity about AI-driven workflows — you follow where the field is going and want to build systems that put AI to work, not just systems that support people doing