Gen AI Python Developer

 Design, build, and maintain data pipelines that deliver structured, AI-ready data to analytical workflows 
and AI agents. 
• Integrate internal and external data sources through APIs, cloud data platforms, and modern agent-tool 
protocols, including credential, entitlement, and environment configuration. 
• Design, migrate, and maintain relational database schemas supporting workflow persistence, metadata, and 
analytical outputs. 
• Extend and modernize analytical and refining databases as coverage grows, with automated update 
processes and clear technical documentation. 
• Normalize and structure raw data so that it can be consumed directly by AI agents within automated 
workflows. 
• Build monitoring and validation that maintains data quality across sources and refresh cycles, including 
completeness, consistency, and timeliness checks. 
• Work with subject matter experts and analysts to understand domain data, its provenance, and its 
limitations. 
• Document data flows, schemas, and integration points so that the work is transferable and auditable. 
• Support deployment and security readiness of data components, including access control, entitlements, and 
audit requirements.
Evaluation and quality:
• Design and build evaluation harnesses that score AI-generated output systematically for accuracy, 
completeness, consistency, and source traceability.
• Establish ground-truth and gold-standard reference sets in partnership with subject matter experts, 
including training and holdout methodology.
• Build regression test suites so that changes to prompts, models, retrieval, or workflow logic are validated 
ahead of release.
• Run structured comparisons across AI models and configurations, measuring quality, latency, and compute 
cost.
• Define and detect the characteristic failure modes of generative systems, including unsupported statements, 
incomplete field population, conflicting values, and retrieval drift.
• Build discrepancy detection that surfaces conflicts between sources as explicit, reviewable flags.
• Work with domain experts to translate expert quality judgments into testable, repeatable criteria.
• Report findings clearly to both engineers and senior leadership, and drive prioritization of the resulting 
improvements.
• Supply evidence of systematic validation to governance, risk, and security review processes.
S&P Global External
August 2026
Core Requirements:
All candidates should be able to demonstrate the following, whatever path they took to acquire it: 
• Proficiency in Python and SQL, sufficient to build both data pipelines and test harnesses and to analyze the 
results independently.
• Practical experience building data pipelines or automated data processes that other people relied on in 
production, including what happened when they failed.
• Working knowledge of relational databases and data modeling, including schema change against systems 
with live consumers.
• Demonstrated experience evaluating, validating, or testing analytical output, models, or systems against a 
defined standard.
• Sound grasp of statistics and experimental design, including sampling, controls, baselines, and sources of 
bias.
• Practical understanding of generative AI applications and prompt engineering, shown through something 
you have built rather than tools you have tried.
• Intellectual honesty and precision: a willingness to report an unwelcome result and defend the method that 
produced it.
• Clear written communication, including the ability to explain a measurement or a data model to someone 
who did not design it.
Backgrounds We Will Consider:
• Data or analytics engineering with production ownership of the pipelines you built.
• AI or machine learning evaluation, large language model evaluation, or applied data science.
• Model validation, model risk management, or quantitative audit in banking, insurance, energy, or a similar 
regulated setting.
• Scientific or academic research background with strong experimental methodology, in any discipline.
• Refining, process, or engineering role in which you validated models or simulations against plant or market 
reality and write code.
• Software quality or test automation engineering with genuine analytica