At Moody's, we unite the brightest minds to turn today’s risks into tomorrow’s opportunities. We do this by striving to create an inclusive environment where everyone feels welcome to be who they are—with the freedom to exchange ideas, think innovatively, and listen to each other and customers in meaningful ways. Moody’s is transforming how the world sees risk. As a global leader in ratings and integrated risk assessment, we’re advancing AI to move from insight to action—enabling intelligence that not only understands complexity but responds to it. We decode risk to unlock opportunity, helping our clients navigate uncertainty with clarity, speed, and confidence.

If you are excited about this opportunity but do not meet every single requirement, please apply! You still may be a great fit for this role or other open roles. We are seeking candidates who model our values: invest in every relationship, lead with curiosity, champion diverse perspectives, turn inputs into actions, and uphold trust through integrity. 

Skills and Competencies

• Possesses more than 7 years of overall experience with 5+ years of hands-on experience in data engineering, with advanced proficiency in Python for building scalable, production-grade systems.
• 3+ years developing production RAG pipelines, including semantic and hierarchical chunking, advanced query rewriting, and multi-query strategies.
• Proficient in hybrid search techniques combining dense retrieval, BM25, filtering, and reranking; skilled in optimizing context windows and balancing latency, recall, and precision trade-offs.
• Well-versed in retrieval evaluation methodologies such as NDCG, MRR, recall@k, precision, and domain-specific assessments.
• Demonstrated experience using Vector DBs (OpenSearch/Elasticsearch, Pinecone, Weaviate, Qdrant, Milvus, etc.), including index tuning, sharding/replication, distributed search, and low-latency retrieval optimization
• Skilled in selecting and adapting embedding models (SBERT, OpenAI, Cohere, Voyage) to domain-specific needs, optimizing for quality, latency, memory footprint, and cost in large-scale real-time applications.
• Knowledgeable in advanced retrieval techniques such as similarity metrics, multi-vector/late interaction architectures, cross-encoder rerankers, and conducting embedding evaluations on domain datasets.
• Well-versed in utilising essential libraries such as Pandas/Polars, NumPy, Pydantic and AI framework/libraries like LangChain, LangGraph, etc.
• Experience in designing and orchestrating large-scale data pipelines with tools such as AirFlow, Prefect, or Dagster for both ETL/ELT, supporting streaming and batch processes.
• Experience using distributed computing frameworks such as Spark, Ray, or Dask and implement monitoring systems to ensure data quality.
• Experience in integrating API both REST/GraphQL based. Implementing Caching to improve the performance using REDIS/MemCached
• Well versed with implementing data privacy and security mechanisms like PII/redaction; secrets/access control.
• Experience in Financial/Structured Finance domain and PDF/OCR/layout/table extraction is preferrable

Education

• Bachelor’s, Master’s, or PhD degree in Computer Science, Information Technology, Artificial Intelligence or related field is required.

Responsibilities

• As the AI Data Engineer within the Asset Management technology teams, you will collaborate with global colleagues to develop, enhance and build AI first products in the Structured Finance area. In this role, you will play a key part in designing and implementing solutions that address complex challenges faced by our clients
• Own the end-to-end design, development, and maintenance of scalable data engineering solutions that power AI-driven products and services
• Architect and implement advanced retrieval systems, including RAG pipelines, to enable accurate, low-latency information access for large-scale applications
• Lead the optimization of search and retrieval workflows, ensuring the right balance between accuracy, performance, and cost across diverse domains
• Establish robust evaluation frameworks and metrics to continuously measure and improve retrieval performance
• Manage and optimize vector database infrastructure to support distributed, high-throughput, and real-time retrieval operations
• Drive the selection, adaptation, and deployment of embedding models tailored to specific business domains and performance requirements
• Design and orchestrate large-scale, reliable data pipelines for both batch and streaming workloads, ensuring data quality and consistency
• Integrate external data sources and APIs into the data ecosystem, implementing caching and performance enhancements where needed
• Champion best practices in data engineering, retrieval system design, and AI framework utilization across the organization
• Monitor, troubleshoot, and optimize data workflows and retrieval systems in production environments
• Communicate project and task progress clearly and regularly with stakeholders, ensuring transparency, alignment, and effective project management throughout the development lifecycle

About the team

You’ll be joining a dynamic, global cross-functional team within Moody’s Analytics Asset Management business, with focus on our Structured Finance group. Our international team collaborates across borders, bringing together diverse expertise to serve some of the world’s most prestigious companies. We specialise in developing cutting-edge software, sophisticated models, and advanced analytics that empower leading organisations to make informed investment decisions. Our Asset Management  team is committed to leveraging the latest advancements in technology, including artificial intelligence, to stay at the forefront of financial technology, consistently delivering robust and future-proof products to our clients. By working alongside talented colleagues worldwide, you’ll help our clients manage risks and seize opportunities in a constantly evolving financial landscape. Join a team celebrated for its culture of innovation, collaborative spirit, and commitment to delivering best-in-class technology solutions to an international clientele

Moody’s is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status, sexual orientation, gender expression, gender identity or any other characteristic protected by law.

Candidates for Moody's Corporation may be asked to disclose securities holdings pursuant to Moody’s Policy for Securities Trading and the requirements of the position. Employment is contingent upon compliance with the Policy, including remediation of positions in those holdings as necessary.