Machine Learning Engineer II
Core
Building agentic AI systems, intelligent agent frameworks, and retrieval methods to synthesize structured and unstructured financial data for S&P Global and third-party clients.
Role type
Mid-level Machine Learning Engineer (Agentic AI & NLP)
Builds
Agentic experiences, deep research workflows, and scalable data-to-insight systems integrated into S&P Global and third-party platforms.
Domain
Financial services, Generative AI, Agentic Systems, Natural Language Processing (NLP)
Deliverable
production ML models | product features
Required skills
Machine learning lifecycle management, NLP techniques, Information retrieval, Python, Agent orchestration design, LLM evaluation
Preferred skills
Context engineering, Memory management, Semantic search, LLM tool utilization
Technologies
LangGraph, pydanticAI, Transformers, HuggingFace, LightGBM, PyTorch, SKLearn, XGBoost, Apache Spark, AWS Athena, Postgres/Pgvector, Arize, Airflow, Docker, Ray, vLLM, FastAPI
Responsibilities
Solve challenges in agentic design and LLM orchestration including context engineering and data access patterns. Participate in all stages of the ML lifecycle from problem framing to production monitoring. Leverage proprietary datasets to build solutions driving business value. Collaborate with Data, Product, Design, and Engineering teams to develop Agents. Work with ML Operations to create automated solutions for the ML systems lifecycle.
Seniority
Mid-level, hands-on IC